import numpy as np
import pandas as pd
import random
random.seed(28)
np.random.seed(28)
import matplotlib.pyplot as plt
from sklearn.metrics import (confusion_matrix, precision_recall_curve, auc,
roc_curve, recall_score, classification_report, f1_score,
precision_recall_fscore_support)
import os
import copy
from sklearn.metrics import mean_absolute_error
pd.options.display.precision = 15
from collections import defaultdict
import lightgbm as lgb
import xgboost as xgb
import time
from collections import Counter
import datetime
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold, KFold, RepeatedKFold, GroupKFold, GridSearchCV, train_test_split, TimeSeriesSplit, RepeatedStratifiedKFold
from sklearn import metrics
import gc
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
from bayes_opt import BayesianOptimization
#import eli5
import shap
from IPython.display import HTML
import json
import matplotlib.pyplot as plt
%matplotlib inline
import os
import time
import datetime
import gc
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
pd.set_option('max_rows', 500)
import re
import os
pd.set_option('display.max_columns', 1000)
pd.set_option('display.max_rows', 500)
pd.set_option('display.width', 1000)
pd.set_option('display.float_format', '{:20,.2f}'.format)
pd.set_option('display.max_colwidth', -1)
np.random.seed(2206)
train = pd.read_csv("../data/training_v2.csv")
samplesubmission = pd.read_csv("../data/samplesubmission.csv")
test = pd.read_csv("../data/unlabeled.csv")
dictionary = pd.read_csv("../data/WiDS Datathon 2020 Dictionary.csv")
solution_template = pd.read_csv("../data/solution_template.csv")
print('train ' , train.shape)
print('test ' , test.shape)
print('samplesubmission ' , samplesubmission.shape)
print('solution_template ' , solution_template.shape)
print('dictionary ' , dictionary.shape)
train (91713, 186) test (39308, 186) samplesubmission (3, 2) solution_template (39308, 2) dictionary (188, 6)
dico = pd.DataFrame(dictionary.T.head(6))
dico.columns=list(dico.loc[dico.index == 'Variable Name'].unstack())
dico = dico.loc[dico.index != 'Variable Name']
dico.columns
train_stat = pd.DataFrame(train.describe())
train_stat2 = pd.concat([dico,train_stat],axis=0)
train_stat2.head(20)
| age | aids | albumin_apache | apache_2_bodysystem | apache_2_diagnosis | apache_3j_bodysystem | apache_3j_diagnosis | apache_4a_hospital_death_prob | apache_4a_icu_death_prob | apache_post_operative | arf_apache | bilirubin_apache | bmi | bun_apache | cirrhosis | creatinine_apache | d1_albumin_max | d1_albumin_min | d1_arterial_pco2_max | d1_arterial_pco2_min | d1_arterial_ph_max | d1_arterial_ph_min | d1_arterial_po2_max | d1_arterial_po2_min | d1_bilirubin_max | d1_bilirubin_min | d1_bun_max | d1_bun_min | d1_calcium_max | d1_calcium_min | d1_creatinine_max | d1_creatinine_min | d1_diasbp_invasive_max | d1_diasbp_invasive_min | d1_diasbp_max | d1_diasbp_min | d1_diasbp_noninvasive_max | d1_diasbp_noninvasive_min | d1_glucose_max | d1_glucose_min | d1_hco3_max | d1_hco3_min | d1_heartrate_max | d1_heartrate_min | d1_hemaglobin_max | d1_hemaglobin_min | d1_hematocrit_max | d1_hematocrit_min | d1_inr_max | d1_inr_min | d1_lactate_max | d1_lactate_min | d1_mbp_invasive_max | d1_mbp_invasive_min | d1_mbp_max | d1_mbp_min | d1_mbp_noninvasive_max | d1_mbp_noninvasive_min | d1_pao2fio2ratio_max | d1_pao2fio2ratio_min | d1_platelets_max | d1_platelets_min | d1_potassium_max | d1_potassium_min | d1_resprate_max | d1_resprate_min | d1_sodium_max | d1_sodium_min | d1_spo2_max | d1_spo2_min | d1_sysbp_invasive_max | d1_sysbp_invasive_min | d1_sysbp_max | d1_sysbp_min | d1_sysbp_noninvasive_max | d1_sysbp_noninvasive_min | d1_temp_max | d1_temp_min | d1_wbc_max | d1_wbc_min | diabetes_mellitus | elective_surgery | encounter_id | ethnicity | fio2_apache | gcs_eyes_apache | gcs_motor_apache | gcs_unable_apache | gcs_verbal_apache | gender | glucose_apache | h1_albumin_max | h1_albumin_min | h1_arterial_pco2_max | h1_arterial_pco2_min | h1_arterial_ph_max | h1_arterial_ph_min | h1_arterial_po2_max | h1_arterial_po2_min | h1_bilirubin_max | h1_bilirubin_min | h1_bun_max | h1_bun_min | h1_calcium_max | h1_calcium_min | h1_creatinine_max | h1_creatinine_min | h1_diasbp_invasive_max | h1_diasbp_invasive_min | h1_diasbp_max | h1_diasbp_min | h1_diasbp_noninvasive_max | h1_diasbp_noninvasive_min | h1_glucose_max | h1_glucose_min | h1_hco3_max | h1_hco3_min | h1_heartrate_max | h1_heartrate_min | h1_hemaglobin_max | h1_hemaglobin_min | h1_hematocrit_max | h1_hematocrit_min | h1_inr_max | h1_inr_min | h1_lactate_max | h1_lactate_min | h1_mbp_invasive_max | h1_mbp_invasive_min | h1_mbp_max | h1_mbp_min | h1_mbp_noninvasive_max | h1_mbp_noninvasive_min | h1_pao2fio2ratio_max | h1_pao2fio2ratio_min | h1_platelets_max | h1_platelets_min | h1_potassium_max | h1_potassium_min | h1_resprate_max | h1_resprate_min | h1_sodium_max | h1_sodium_min | h1_spo2_max | h1_spo2_min | h1_sysbp_invasive_max | h1_sysbp_invasive_min | h1_sysbp_max | h1_sysbp_min | h1_sysbp_noninvasive_max | h1_sysbp_noninvasive_min | h1_temp_max | h1_temp_min | h1_wbc_max | h1_wbc_min | heart_rate_apache | height | hematocrit_apache | hepatic_failure | hospital_admit_source | hospital_death | hospital_id | icu_admit_source | icu_admit_type | icu_id | icu_stay_type | icu_type | immunosuppression | intubated_apache | leukemia | lymphoma | map_apache | paco2_apache | paco2_for_ph_apache | pao2_apache | patient_id | ph_apache | pre_icu_los_days | pred | readmission_status | resprate_apache | sodium_apache | solid_tumor_with_metastasis | temp_apache | urineoutput_apache | ventilated_apache | wbc_apache | weight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Category | demographic | APACHE comorbidity | APACHE covariate | APACHE grouping | APACHE covariate | APACHE grouping | APACHE covariate | APACHE prediction | APACHE prediction | APACHE covariate | APACHE covariate | APACHE covariate | demographic | APACHE covariate | APACHE comorbidity | APACHE covariate | labs | labs | labs blood gas | labs blood gas | labs blood gas | labs blood gas | labs blood gas | labs blood gas | labs | labs | labs | labs | labs | labs | labs | labs | vitals | vitals | vitals | vitals | vitals | vitals | labs | labs | labs | labs | vitals | vitals | labs | labs | labs | labs | labs | labs | labs | labs | vitals | vitals | vitals | vitals | vitals | vitals | labs blood gas | labs blood gas | labs | labs | labs | labs | vitals | vitals | labs | labs | vitals | vitals | vitals | vitals | vitals | vitals | vitals | vitals | vitals | vitals | labs | labs | APACHE comorbidity | demographic | identifier | demographic | APACHE covariate | APACHE covariate | APACHE covariate | APACHE covariate | APACHE covariate | demographic | APACHE covariate | labs | labs | labs blood gas | labs blood gas | labs blood gas | labs blood gas | labs blood gas | labs blood gas | labs | labs | labs | labs | labs | labs | labs | labs | vitals | vitals | vitals | vitals | vitals | vitals | labs | labs | labs | labs | vitals | vitals | labs | labs | labs | labs | labs | labs | labs | labs | vitals | vitals | vitals | vitals | vitals | vitals | labs blood gas | labs blood gas | labs | labs | labs | labs | vitals | vitals | labs | labs | vitals | vitals | vitals | vitals | vitals | vitals | vitals | vitals | vitals | vitals | labs | labs | APACHE covariate | demographic | APACHE covariate | APACHE comorbidity | demographic | demographic | identifier | demographic | demographic | demographic | demographic | demographic | APACHE comorbidity | APACHE covariate | APACHE comorbidity | APACHE comorbidity | APACHE covariate | APACHE covariate | APACHE covariate | APACHE covariate | identifier | APACHE covariate | demographic | GOSSIS example prediction | demographic | APACHE covariate | APACHE covariate | APACHE comorbidity | APACHE covariate | APACHE covariate | APACHE covariate | APACHE covariate | demographic |
| Unit of Measure | Years | None | g/L | None | None | None | None | None | None | None | None | micromol/L | kilograms/metres^2 | mmol/L | None | micromol/L | None | g/L | Millimetres of mercury | Millimetres of mercury | None | None | Millimetres of mercury | Millimetres of mercury | micromol/L | micromol/L | mmol/L | mmol/L | mmol/L | mmol/L | micromol/L | micromol/L | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | mmol/L | mmol/L | mmol/L | None | Beats per minute | Beats per minute | g/dL | g/dL | Fraction | Fraction | micromol/L | micromol/L | mmol/L | mmol/L | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Fraction | Fraction | 10^9/L | 10^9/L | mmol/L | mmol/L | Breaths per minute | Breaths per minute | mmol/L | mmol/L | Percentage | Percentage | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Degrees Celsius | Degrees Celsius | 10^9/L | 10^9/L | None | None | None | None | Fraction | None | None | None | None | None | mmol/L | None | g/L | Millimetres of mercury | Millimetres of mercury | None | None | Millimetres of mercury | Millimetres of mercury | micromol/L | micromol/L | mmol/L | mmol/L | mmol/L | mmol/L | micromol/L | micromol/L | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | mmol/L | mmol/L | mmol/L | None | Beats per minute | Beats per minute | g/dL | g/dL | Fraction | Fraction | micromol/L | micromol/L | mmol/L | mmol/L | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Fraction | Fraction | 10^9/L | 10^9/L | mmol/L | mmol/L | Breaths per minute | Breaths per minute | mmol/L | mmol/L | Percentage | Percentage | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Degrees Celsius | Degrees Celsius | 10^9/L | 10^9/L | Beats per minute | centimetres | Fraction | None | None | None | None | None | None | None | None | None | None | None | None | None | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | Millimetres of mercury | None | None | Days | None | None | Breaths per minute | mmol/L | None | Degrees Celsius | Millilitres | None | 10^9/L | kilograms |
| Data Type | numeric | binary | numeric | string | string | string | string | numeric | numeric | binary | binary | numeric | string | numeric | binary | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | binary | binary | integer | string | numeric | integer | integer | binary | integer | string | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | binary | string | binary | integer | string | string | integer | string | string | binary | binary | binary | binary | numeric | numeric | numeric | numeric | integer | numeric | numeric | numeric | binary | numeric | numeric | binary | numeric | numeric | binary | numeric | numeric |
| Description | The age of the patient on unit admission | Whether the patient has a definitive diagnosis of acquired immune deficiency syndrome (AIDS) (not HIV positive alone) | The albumin concentration measured during the first 24 hours which results in the highest APACHE III score | Admission diagnosis group for APACHE II | The APACHE II diagnosis for the ICU admission | Admission diagnosis group for APACHE III | The APACHE III-J sub-diagnosis code which best describes the reason for the ICU admission | The APACHE IVa probabilistic prediction of in-hospital mortality for the patient which utilizes the APACHE III score and other covariates, including diagnosis. | The APACHE IVa probabilistic prediction of in ICU mortality for the patient which utilizes the APACHE III score and other covariates, including diagnosis | The APACHE operative status; 1 for post-operative, 0 for non-operative | Whether the patient had acute renal failure during the first 24 hours of their unit stay, defined as a 24 hour urine output <410ml, creatinine >=133 micromol/L and no chronic dialysis | The bilirubin concentration measured during the first 24 hours which results in the highest APACHE III score | The body mass index of the person on unit admission | The blood urea nitrogen concentration measured during the first 24 hours which results in the highest APACHE III score | Whether the patient has a history of heavy alcohol use with portal hypertension and varices, other causes of cirrhosis with evidence of portal hypertension and varices, or biopsy proven cirrhosis. This comorbidity does not apply to patients with a functioning liver transplant. | The creatinine concentration measured during the first 24 hours which results in the highest APACHE III score | The lowest albumin concentration of the patient in their serum during the first 24 hours of their unit stay | The lowest albumin concentration of the patient in their serum during the first 24 hours of their unit stay | The highest arterial partial pressure of carbon dioxide for the patient during the first 24 hours of their unit stay | The lowest arterial partial pressure of carbon dioxide for the patient during the first 24 hours of their unit stay | The highest arterial pH for the patient during the first 24 hours of their unit stay | The lowest arterial pH for the patient during the first 24 hours of their unit stay | The highest arterial partial pressure of oxygen for the patient during the first 24 hours of their unit stay | The lowest arterial partial pressure of oxygen for the patient during the first 24 hours of their unit stay | The highest bilirubin concentration of the patient in their serum or plasma during the first 24 hours of their unit stay | The lowest bilirubin concentration of the patient in their serum or plasma during the first 24 hours of their unit stay | The highest blood urea nitrogen concentration of the patient in their serum or plasma during the first 24 hours of their unit stay | The lowest blood urea nitrogen concentration of the patient in their serum or plasma during the first 24 hours of their unit stay | The highest calcium concentration of the patient in their serum during the first 24 hours of their unit stay | The lowest calcium concentration of the patient in their serum during the first 24 hours of their unit stay | The highest creatinine concentration of the patient in their serum or plasma during the first 24 hours of their unit stay | The lowest creatinine concentration of the patient in their serum or plasma during the first 24 hours of their unit stay | The patient's highest diastolic blood pressure during the first 24 hours of their unit stay, invasively measured | The patient's lowest diastolic blood pressure during the first 24 hours of their unit stay, invasively measured | The patient's highest diastolic blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured | The patient's lowest diastolic blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured | The patient's highest diastolic blood pressure during the first 24 hours of their unit stay, non-invasively measured | The patient's lowest diastolic blood pressure during the first 24 hours of their unit stay, non-invasively measured | The highest glucose concentration of the patient in their serum or plasma during the first 24 hours of their unit stay | The lowest glucose concentration of the patient in their serum or plasma during the first 24 hours of their unit stay | The highest bicarbonate concentration for the patient in their serum or plasma during the first 24 hours of their unit stay | The lowest bicarbonate concentration for the patient in their serum or plasma during the first 24 hours of their unit stay | The patient's highest heart rate during the first 24 hours of their unit stay | The patient's lowest heart rate during the first 24 hours of their unit stay | The highest hemoglobin concentration for the patient during the first 24 hours of their unit stay | The lowest hemoglobin concentration for the patient during the first 24 hours of their unit stay | The highest volume proportion of red blood cells in a patient's blood during the first 24 hours of their unit stay, expressed as a fraction | The lowest volume proportion of red blood cells in a patient's blood during the first 24 hours of their unit stay, expressed as a fraction | The highest international normalized ratio for the patient during the first 24 hours of their unit stay | The lowest international normalized ratio for the patient during the first 24 hours of their unit stay | The highest lactate concentration for the patient in their serum or plasma during the first 24 hours of their unit stay | The lowest lactate concentration for the patient in their serum or plasma during the first 24 hours of their unit stay | The patient's highest mean blood pressure during the first 24 hours of their unit stay, invasively measured | The patient's lowest mean blood pressure during the first 24 hours of their unit stay, invasively measured | The patient's highest mean blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured | The patient's lowest mean blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured | The patient's highest mean blood pressure during the first 24 hours of their unit stay, non-invasively measured | The patient's lowest mean blood pressure during the first 24 hours of their unit stay, non-invasively measured | The highest fraction of inspired oxygen for the patient during the first 24 hours of their unit stay | The lowest fraction of inspired oxygen for the patient during the first 24 hours of their unit stay | The highest platelet count for the patient during the first 24 hours of their unit stay | The lowest platelet count for the patient during the first 24 hours of their unit stay | The highest potassium concentration for the patient in their serum or plasma during the first 24 hours of their unit stay | The lowest potassium concentration for the patient in their serum or plasma during the first 24 hours of their unit stay | The patient's highest respiratory rate during the first 24 hours of their unit stay | The patient's lowest respiratory rate during the first 24 hours of their unit stay | The highest sodium concentration for the patient in their serum or plasma during the first 24 hours of their unit stay | The lowest sodium concentration for the patient in their serum or plasma during the first 24 hours of their unit stay | The patient's highest peripheral oxygen saturation during the first 24 hours of their unit stay | The patient's lowest peripheral oxygen saturation during the first 24 hours of their unit stay | The patient's highest systolic blood pressure during the first 24 hours of their unit stay, invasively measured | The patient's lowest systolic blood pressure during the first 24 hours of their unit stay, invasively measured | The patient's highest systolic blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured | The patient's lowest systolic blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured | The patient's highest systolic blood pressure during the first 24 hours of their unit stay, non-invasively measured | The patient's lowest systolic blood pressure during the first 24 hours of their unit stay, non-invasively measured | The patient's highest core temperature during the first 24 hours of their unit stay, invasively measured | The patient's lowest core temperature during the first 24 hours of their unit stay | The highest white blood cell count for the patient during the first 24 hours of their unit stay | The lowest white blood cell count for the patient during the first 24 hours of their unit stay | Whether the patient has been diagnosed with diabetes, either juvenile or adult onset, which requires medication. | Whether the patient was admitted to the hospital for an elective surgical operation | Unique identifier associated with a patient unit stay | The common national or cultural tradition which the person belongs to | The fraction of inspired oxygen from the arterial blood gas taken during the first 24 hours of unit admission which produces the highest APACHE III score for oxygenation | The eye opening component of the Glasgow Coma Scale measured during the first 24 hours which results in the highest APACHE III score | The motor component of the Glasgow Coma Scale measured during the first 24 hours which results in the highest APACHE III score | Whether the Glasgow Coma Scale was unable to be assessed due to patient sedation | The verbal component of the Glasgow Coma Scale measured during the first 24 hours which results in the highest APACHE III score | The genotypical sex of the patient | The glucose concentration measured during the first 24 hours which results in the highest APACHE III score | The lowest albumin concentration of the patient in their serum during the first hour of their unit stay | The lowest albumin concentration of the patient in their serum during the first hour of their unit stay | The highest arterial partial pressure of carbon dioxide for the patient during the first hour of their unit stay | The lowest arterial partial pressure of carbon dioxide for the patient during the first hour of their unit stay | The highest arterial pH for the patient during the first hour of their unit stay | The lowest arterial pH for the patient during the first hour of their unit stay | The highest arterial partial pressure of oxygen for the patient during the first hour of their unit stay | The lowest arterial partial pressure of oxygen for the patient during the first hour of their unit stay | The highest bilirubin concentration of the patient in their serum or plasma during the first hour of their unit stay | The lowest bilirubin concentration of the patient in their serum or plasma during the first hour of their unit stay | The highest blood urea nitrogen concentration of the patient in their serum or plasma during the first hour of their unit stay | The lowest blood urea nitrogen concentration of the patient in their serum or plasma during the first hour of their unit stay | The highest calcium concentration of the patient in their serum during the first hour of their unit stay | The lowest calcium concentration of the patient in their serum during the first hour of their unit stay | The highest creatinine concentration of the patient in their serum or plasma during the first hour of their unit stay | The lowest creatinine concentration of the patient in their serum or plasma during the first hour of their unit stay | The patient's highest diastolic blood pressure during the first hour of their unit stay, invasively measured | The patient's lowest diastolic blood pressure during the first hour of their unit stay, invasively measured | The patient's highest diastolic blood pressure during the first hour of their unit stay, either non-invasively or invasively measured | The patient's lowest diastolic blood pressure during the first hour of their unit stay, either non-invasively or invasively measured | The patient's highest diastolic blood pressure during the first hour of their unit stay, non-invasively measured | The patient's lowest diastolic blood pressure during the first hour of their unit stay, non-invasively measured | The highest glucose concentration of the patient in their serum or plasma during the first hour of their unit stay | The lowest glucose concentration of the patient in their serum or plasma during the first hour of their unit stay | The highest bicarbonate concentration for the patient in their serum or plasma during the first hour of their unit stay | The lowest bicarbonate concentration for the patient in their serum or plasma during the first hour of their unit stay | The patient's highest heart rate during the first hour of their unit stay | The patient's lowest heart rate during the first hour of their unit stay | The highest hemoglobin concentration for the patient during the first hour of their unit stay | The lowest hemoglobin concentration for the patient during the first hour of their unit stay | The highest volume proportion of red blood cells in a patient's blood during the first hour of their unit stay, expressed as a fraction | The lowest volume proportion of red blood cells in a patient's blood during the first hour of their unit stay, expressed as a fraction | The highest international normalized ratio for the patient during the first hour of their unit stay | The lowest international normalized ratio for the patient during the first hour of their unit stay | The highest lactate concentration for the patient in their serum or plasma during the first hour of their unit stay | The lowest lactate concentration for the patient in their serum or plasma during the first hour of their unit stay | The patient's highest mean blood pressure during the first hour of their unit stay, invasively measured | The patient's lowest mean blood pressure during the first hour of their unit stay, invasively measured | The patient's highest mean blood pressure during the first hour of their unit stay, either non-invasively or invasively measured | The patient's lowest mean blood pressure during the first hour of their unit stay, either non-invasively or invasively measured | The patient's highest mean blood pressure during the first hour of their unit stay, non-invasively measured | The patient's lowest mean blood pressure during the first hour of their unit stay, non-invasively measured | The highest fraction of inspired oxygen for the patient during the first hour of their unit stay | The lowest fraction of inspired oxygen for the patient during the first hour of their unit stay | The highest platelet count for the patient during the first hour of their unit stay | The lowest platelet count for the patient during the first hour of their unit stay | The highest potassium concentration for the patient in their serum or plasma during the first hour of their unit stay | The lowest potassium concentration for the patient in their serum or plasma during the first hour of their unit stay | The patient's highest respiratory rate during the first hour of their unit stay | The patient's lowest respiratory rate during the first hour of their unit stay | The highest sodium concentration for the patient in their serum or plasma during the first hour of their unit stay | The lowest sodium concentration for the patient in their serum or plasma during the first hour of their unit stay | The patient's highest peripheral oxygen saturation during the first hour of their unit stay | The patient's lowest peripheral oxygen saturation during the first hour of their unit stay | The patient's highest systolic blood pressure during the first hour of their unit stay, invasively measured | The patient's lowest systolic blood pressure during the first hour of their unit stay, invasively measured | The patient's highest systolic blood pressure during the first hour of their unit stay, either non-invasively or invasively measured | The patient's lowest systolic blood pressure during the first hour of their unit stay, either non-invasively or invasively measured | The patient's highest systolic blood pressure during the first hour of their unit stay, non-invasively measured | The patient's lowest systolic blood pressure during the first hour of their unit stay, non-invasively measured | The patient's highest core temperature during the first hour of their unit stay, invasively measured | The patient's lowest core temperature during the first hour of their unit stay | The highest white blood cell count for the patient during the first hour of their unit stay | The lowest white blood cell count for the patient during the first hour of their unit stay | The heart rate measured during the first 24 hours which results in the highest APACHE III score | The height of the person on unit admission | The hematocrit measured during the first 24 hours which results in the highest APACHE III score | Whether the patient has cirrhosis and additional complications including jaundice and ascites, upper GI bleeding, hepatic encephalopathy, or coma. | The location of the patient prior to being admitted to the hospital | Whether the patient died during this hospitalization | Unique identifier associated with a hospital | The location of the patient prior to being admitted to the unit | The type of unit admission for the patient | A unique identifier for the unit to which the patient was admitted | NaN | A classification which indicates the type of care the unit is capable of providing | Whether the patient has their immune system suppressed within six months prior to ICU admission for any of the following reasons; radiation therapy, chemotherapy, use of non-cytotoxic immunosuppressive drugs, high dose steroids (at least 0.3 mg/kg/day of methylprednisolone or equivalent for at least 6 months). | Whether the patient was intubated at the time of the highest scoring arterial blood gas used in the oxygenation score | Whether the patient has been diagnosed with acute or chronic myelogenous leukemia, acute or chronic lymphocytic leukemia, or multiple myeloma. | Whether the patient has been diagnosed with non-Hodgkin lymphoma. | The mean arterial pressure measured during the first 24 hours which results in the highest APACHE III score | The partial pressure of carbon dioxide from the arterial blood gas taken during the first 24 hours of unit admission which produces the highest APACHE III score for oxygenation | The partial pressure of carbon dioxide from the arterial blood gas taken during the first 24 hours of unit admission which produces the highest APACHE III score for acid-base disturbance | The partial pressure of oxygen from the arterial blood gas taken during the first 24 hours of unit admission which produces the highest APACHE III score for oxygenation | Unique identifier associated with a patient | The pH from the arterial blood gas taken during the first 24 hours of unit admission which produces the highest APACHE III score for acid-base disturbance | The length of stay of the patient between hospital admission and unit admission | Example mortality prediction, shared as a 'baseline' based on one of the GOSSIS algorithm development models. | Whether the current unit stay is the second (or greater) stay at an ICU within the same hospitalization | The respiratory rate measured during the first 24 hours which results in the highest APACHE III score | The sodium concentration measured during the first 24 hours which results in the highest APACHE III score | Whether the patient has been diagnosed with any solid tumor carcinoma (including malignant melanoma) which has evidence of metastasis. | The temperature measured during the first 24 hours which results in the highest APACHE III score | The total urine output for the first 24 hours | Whether the patient was invasively ventilated at the time of the highest scoring arterial blood gas using the oxygenation scoring algorithm, including any mode of positive pressure ventilation delivered through a circuit attached to an endo-tracheal tube or tracheostomy | The white blood cell count measured during the first 24 hours which results in the highest APACHE III score | The weight (body mass) of the person on unit admission |
| Example | None | 1 | 30 | Respiratory | 308 | Cardiovascular | 1405 | 0.31 | 0.24 | 1 | 0 | 20 | 21.5 | None | 1 | 70 | 30 | 30 | 40 | 40 | 7.4 | 7.4 | 80 | 80 | 20 | 20 | 5 | 5 | 2.5 | 2.5 | 70 | 70 | 60 | 60 | 60 | 60 | 60 | 60 | 5 | 5 | 30 | 30 | 75 | 75 | 10 | 10 | 0.4 | 0.4 | 1 | 1 | 1 | 1 | 80 | 80 | 80 | 80 | 80 | 80 | 0.21 | 0.21 | 200 | 200 | 3.8 | 3.8 | 14 | 14 | 145 | 145 | None | 100 | 120 | 120 | 120 | 120 | 120 | 120 | 33 | 33 | 10 | 10 | 1 | 0 | None | Caucasian | 0.21 | 4 | 6 | 1 | 5 | F | 5 | 30 | 30 | 40 | 40 | 7.4 | 7.4 | 80 | 80 | 20 | 20 | 5 | 5 | 2.5 | 2.5 | 70 | 70 | 60 | 60 | 60 | 60 | 60 | 60 | 5 | 5 | 30 | 30 | 75 | 75 | 10 | 10 | 0.4 | 0.4 | 1 | 1 | 1 | 1 | 80 | 80 | 80 | 80 | 80 | 80 | 0.21 | 0.21 | 200 | 200 | 3.8 | 3.8 | 14 | 14 | 145 | 145 | None | 100 | 120 | 120 | 120 | 120 | 120 | 120 | 33 | 33 | 10 | 10 | 75 | 180 | 0.4 | 1 | Home | 0 | None | Operating room | Cardiothoracic | None | None | Neurological ICU | 1 | 0 | 1 | 1 | None | 40 | 40 | 80 | None | 7.4 | 3.5 | 0.000921 | 0 | 14 | 145 | 1 | 33 | 2000 | 1 | 10 | 80 |
| count | 87,485.00 | 90,998.00 | 37,334.00 | NaN | 90,051.00 | NaN | 90,612.00 | 83,766.00 | 83,766.00 | 91,713.00 | 90,998.00 | 33,579.00 | 88,284.00 | 72,451.00 | 90,998.00 | 72,860.00 | 42,617.00 | 42,617.00 | 32,442.00 | 32,442.00 | 31,590.00 | 31,590.00 | 32,451.00 | 32,451.00 | 38,040.00 | 38,040.00 | 81,199.00 | 81,199.00 | 78,644.00 | 78,644.00 | 81,544.00 | 81,544.00 | 23,729.00 | 23,729.00 | 91,548.00 | 91,548.00 | 90,673.00 | 90,673.00 | 85,906.00 | 85,906.00 | 76,642.00 | 76,642.00 | 91,568.00 | 91,568.00 | 79,566.00 | 79,566.00 | 80,059.00 | 80,059.00 | 33,772.00 | 33,772.00 | 23,317.00 | 23,317.00 | 23,936.00 | 23,936.00 | 91,493.00 | 91,493.00 | 90,234.00 | 90,234.00 | 25,705.00 | 25,705.00 | 78,269.00 | 78,269.00 | 82,128.00 | 82,128.00 | 91,328.00 | 91,328.00 | 81,518.00 | 81,518.00 | 91,380.00 | 91,380.00 | 23,754.00 | 23,754.00 | 91,554.00 | 91,554.00 | 90,686.00 | 90,686.00 | 89,389.00 | 89,389.00 | 78,539.00 | 78,539.00 | 90,998.00 | 91,713.00 | 91,713.00 | NaN | 20,845.00 | 89,812.00 | 89,812.00 | 90,676.00 | 89,812.00 | NaN | 80,677.00 | 7,889.00 | 7,889.00 | 15,754.00 | 15,754.00 | 15,289.00 | 15,289.00 | 15,768.00 | 15,768.00 | 7,094.00 | 7,094.00 | 16,622.00 | 16,622.00 | 15,850.00 | 15,850.00 | 16,756.00 | 16,756.00 | 16,785.00 | 16,785.00 | 88,094.00 | 88,094.00 | 84,363.00 | 84,363.00 | 39,099.00 | 39,099.00 | 15,619.00 | 15,619.00 | 88,923.00 | 88,923.00 | 18,590.00 | 18,590.00 | 18,293.00 | 18,293.00 | 33,772.00 | 33,772.00 | 7,344.00 | 7,344.00 | 16,869.00 | 16,869.00 | 87,074.00 | 87,074.00 | 82,629.00 | 82,629.00 | 11,518.00 | 11,518.00 | 16,040.00 | 16,040.00 | 19,611.00 | 19,611.00 | 87,356.00 | 87,356.00 | 19,096.00 | 19,096.00 | 87,528.00 | 87,528.00 | 16,798.00 | 16,798.00 | 88,102.00 | 88,102.00 | 84,372.00 | 84,372.00 | 69,981.00 | 69,981.00 | 15,760.00 | 15,760.00 | 90,835.00 | 90,379.00 | 71,835.00 | 90,998.00 | NaN | 91,713.00 | 91,713.00 | NaN | NaN | 91,713.00 | NaN | NaN | 90,998.00 | 90,998.00 | 90,998.00 | 90,998.00 | 90,719.00 | 20,845.00 | 20,845.00 | 20,845.00 | 91,713.00 | 20,845.00 | 91,713.00 | NaN | 91,713.00 | 90,479.00 | 73,113.00 | 90,998.00 | 87,605.00 | 42,715.00 | 90,998.00 | 69,701.00 | 88,993.00 |
| mean | 62.31 | 0.00 | 2.90 | NaN | 185.40 | NaN | 558.22 | 0.09 | 0.04 | 0.20 | 0.03 | 1.15 | 29.19 | 25.83 | 0.02 | 1.48 | 2.97 | 2.90 | 45.25 | 38.43 | 7.39 | 7.32 | 165.91 | 103.51 | 1.14 | 1.07 | 25.69 | 23.77 | 8.38 | 8.18 | 1.49 | 1.37 | 78.76 | 46.74 | 88.49 | 50.16 | 88.61 | 50.24 | 174.64 | 114.38 | 24.37 | 23.17 | 103.00 | 70.32 | 11.45 | 10.89 | 34.53 | 32.95 | 1.60 | 1.48 | 2.93 | 2.13 | 114.89 | 62.32 | 104.65 | 64.87 | 104.59 | 64.94 | 285.67 | 223.52 | 207.11 | 196.77 | 4.25 | 3.93 | 28.88 | 12.85 | 139.12 | 137.72 | 99.24 | 90.45 | 154.27 | 93.81 | 148.34 | 96.92 | 148.24 | 96.99 | 37.28 | 36.27 | 12.48 | 11.31 | 0.23 | 0.18 | 65,606.08 | NaN | 0.60 | 3.47 | 5.47 | 0.01 | 3.99 | NaN | 160.33 | 3.03 | 3.03 | 44.67 | 43.38 | 7.34 | 7.33 | 163.84 | 144.15 | 1.10 | 1.10 | 25.84 | 25.82 | 8.28 | 8.28 | 1.53 | 1.53 | 67.97 | 56.14 | 75.35 | 62.84 | 75.81 | 63.27 | 167.99 | 159.22 | 22.50 | 22.42 | 92.23 | 83.66 | 11.19 | 11.04 | 33.67 | 33.22 | 1.60 | 1.48 | 3.07 | 3.02 | 94.88 | 75.97 | 91.61 | 79.40 | 91.59 | 79.71 | 244.40 | 235.93 | 196.10 | 195.48 | 4.20 | 4.15 | 22.63 | 17.21 | 138.24 | 137.90 | 98.04 | 95.17 | 138.70 | 114.83 | 133.25 | 116.36 | 133.05 | 116.55 | 36.71 | 36.61 | 13.46 | 13.42 | 99.71 | 169.64 | 32.99 | 0.01 | NaN | 0.09 | 105.67 | NaN | NaN | 508.36 | NaN | NaN | 0.03 | 0.15 | 0.01 | 0.00 | 88.02 | 42.18 | 42.18 | 131.15 | 65,537.13 | 7.35 | 0.84 | NaN | 0.00 | 25.81 | 137.97 | 0.02 | 36.41 | 1,738.28 | 0.33 | 12.13 | 84.03 |
| std | 16.78 | 0.03 | 0.68 | NaN | 86.05 | NaN | 463.27 | 0.25 | 0.22 | 0.40 | 0.16 | 2.17 | 8.28 | 20.67 | 0.12 | 1.53 | 0.67 | 0.67 | 14.67 | 10.94 | 0.08 | 0.11 | 108.01 | 61.85 | 2.13 | 2.02 | 20.47 | 18.80 | 0.74 | 0.78 | 1.51 | 1.33 | 21.73 | 12.86 | 19.80 | 13.32 | 19.79 | 13.34 | 86.69 | 38.27 | 4.37 | 4.99 | 22.02 | 17.12 | 2.17 | 2.36 | 6.24 | 6.85 | 0.96 | 0.75 | 3.08 | 2.11 | 49.45 | 18.06 | 20.81 | 15.68 | 20.70 | 15.70 | 128.22 | 117.55 | 89.63 | 88.18 | 0.67 | 0.58 | 10.70 | 5.06 | 4.82 | 4.92 | 1.79 | 10.03 | 32.29 | 24.98 | 25.73 | 20.68 | 25.79 | 20.71 | 0.69 | 0.75 | 6.80 | 5.95 | 0.42 | 0.39 | 37,795.09 | NaN | 0.26 | 0.95 | 1.29 | 0.10 | 1.56 | NaN | 90.79 | 0.73 | 0.73 | 14.63 | 14.11 | 0.11 | 0.11 | 113.46 | 98.46 | 2.03 | 2.03 | 21.44 | 21.42 | 0.88 | 0.89 | 1.58 | 1.57 | 16.26 | 14.14 | 18.41 | 16.36 | 18.48 | 16.42 | 94.72 | 89.16 | 5.21 | 5.21 | 21.82 | 20.28 | 2.37 | 2.41 | 6.84 | 7.03 | 0.96 | 0.75 | 2.93 | 2.88 | 30.81 | 19.23 | 20.53 | 19.13 | 20.55 | 19.24 | 129.96 | 126.46 | 92.65 | 92.78 | 0.76 | 0.75 | 7.52 | 6.07 | 5.75 | 5.68 | 3.21 | 6.63 | 29.21 | 27.97 | 27.56 | 26.51 | 27.68 | 26.62 | 0.75 | 0.78 | 6.98 | 6.97 | 30.87 | 10.80 | 6.87 | 0.11 | NaN | 0.28 | 62.85 | NaN | NaN | 228.99 | NaN | NaN | 0.16 | 0.36 | 0.08 | 0.06 | 42.03 | 12.38 | 12.38 | 83.61 | 37,811.25 | 0.10 | 2.49 | NaN | 0.00 | 15.11 | 5.28 | 0.14 | 0.83 | 1,448.16 | 0.47 | 6.92 | 25.01 |
| min | 16.00 | 0.00 | 1.20 | NaN | 101.00 | NaN | 0.01 | -1.00 | -1.00 | 0.00 | 0.00 | 0.10 | 14.84 | 4.00 | 0.00 | 0.30 | 1.20 | 1.10 | 18.40 | 14.90 | 7.05 | 6.89 | 39.00 | 28.00 | 0.20 | 0.20 | 4.00 | 3.00 | 6.20 | 5.50 | 0.34 | 0.30 | 37.00 | 5.00 | 46.00 | 13.00 | 46.00 | 13.00 | 73.00 | 33.00 | 12.00 | 7.00 | 58.00 | 0.00 | 6.80 | 5.30 | 20.40 | 16.10 | 0.90 | 0.90 | 0.40 | 0.40 | 38.00 | 2.00 | 60.00 | 22.00 | 60.00 | 22.00 | 54.80 | 36.00 | 27.00 | 18.55 | 2.80 | 2.40 | 14.00 | 0.00 | 123.00 | 117.00 | 0.00 | 0.00 | 71.00 | 10.00 | 90.00 | 41.00 | 90.00 | 41.03 | 35.10 | 31.89 | 1.20 | 0.90 | 0.00 | 0.00 | 1.00 | NaN | 0.21 | 1.00 | 1.00 | 0.00 | 1.00 | NaN | 39.00 | 1.10 | 1.10 | 15.00 | 15.00 | 6.93 | 6.90 | 34.00 | 31.00 | 0.20 | 0.20 | 4.00 | 4.00 | 5.60 | 5.30 | 0.33 | 0.33 | 33.00 | 19.00 | 37.00 | 22.00 | 37.00 | 22.00 | 59.00 | 42.00 | 6.00 | 6.00 | 46.00 | 36.00 | 5.10 | 5.00 | 16.00 | 15.50 | 0.90 | 0.90 | 0.40 | 0.40 | 35.62 | 8.00 | 49.00 | 32.00 | 49.00 | 32.00 | 42.00 | 38.00 | 20.00 | 20.00 | 2.50 | 2.50 | 10.00 | 0.00 | 114.00 | 114.00 | 0.00 | 0.00 | 65.00 | 31.44 | 75.00 | 53.00 | 75.00 | 53.00 | 33.40 | 32.90 | 1.10 | 1.09 | 30.00 | 137.20 | 16.20 | 0.00 | NaN | 0.00 | 2.00 | NaN | NaN | 82.00 | NaN | NaN | 0.00 | 0.00 | 0.00 | 0.00 | 40.00 | 18.00 | 18.00 | 31.00 | 1.00 | 6.96 | -24.95 | NaN | 0.00 | 4.00 | 117.00 | 0.00 | 32.10 | 0.00 | 0.00 | 0.90 | 38.60 |
| 25% | 52.00 | 0.00 | 2.40 | NaN | 113.00 | NaN | 203.01 | 0.02 | 0.01 | 0.00 | 0.00 | 0.40 | 23.64 | 13.00 | 0.00 | 0.72 | 2.50 | 2.40 | 36.00 | 32.00 | 7.34 | 7.27 | 88.10 | 69.00 | 0.40 | 0.40 | 13.00 | 12.00 | 7.90 | 7.70 | 0.76 | 0.71 | 65.00 | 39.00 | 75.00 | 42.00 | 75.00 | 42.00 | 117.00 | 91.00 | 22.00 | 21.00 | 87.00 | 60.00 | 9.80 | 9.20 | 30.00 | 28.00 | 1.10 | 1.10 | 1.20 | 1.00 | 89.00 | 54.00 | 90.00 | 55.00 | 90.00 | 55.00 | 192.29 | 132.50 | 148.00 | 138.00 | 3.80 | 3.60 | 22.00 | 10.00 | 137.00 | 135.00 | 99.00 | 89.00 | 134.00 | 80.00 | 130.00 | 83.00 | 130.00 | 84.00 | 36.90 | 36.10 | 8.00 | 7.40 | 0.00 | 0.00 | 32,852.00 | NaN | 0.40 | 3.00 | 6.00 | 0.00 | 4.00 | NaN | 97.00 | 2.50 | 2.50 | 36.00 | 35.00 | 7.29 | 7.28 | 80.70 | 77.00 | 0.40 | 0.40 | 13.00 | 13.00 | 7.70 | 7.70 | 0.79 | 0.79 | 57.00 | 46.00 | 62.00 | 52.00 | 63.00 | 52.00 | 111.00 | 106.00 | 20.00 | 20.00 | 77.00 | 69.00 | 9.50 | 9.30 | 28.90 | 28.10 | 1.10 | 1.10 | 1.30 | 1.30 | 78.00 | 63.00 | 77.00 | 66.00 | 77.00 | 66.00 | 142.00 | 136.00 | 133.00 | 132.00 | 3.70 | 3.70 | 18.00 | 14.00 | 136.00 | 135.00 | 97.00 | 94.00 | 119.00 | 95.00 | 113.00 | 98.00 | 113.00 | 98.00 | 36.40 | 36.30 | 8.60 | 8.60 | 86.00 | 162.50 | 28.00 | 0.00 | NaN | 0.00 | 47.00 | NaN | NaN | 369.00 | NaN | NaN | 0.00 | 0.00 | 0.00 | 0.00 | 54.00 | 34.40 | 34.40 | 77.50 | 32,830.00 | 7.31 | 0.04 | NaN | 0.00 | 11.00 | 135.00 | 0.00 | 36.20 | 740.36 | 0.00 | 7.50 | 66.80 |
| 50% | 65.00 | 0.00 | 2.90 | NaN | 122.00 | NaN | 409.02 | 0.05 | 0.02 | 0.00 | 0.00 | 0.60 | 27.65 | 19.00 | 0.00 | 0.98 | 3.00 | 2.90 | 42.80 | 37.00 | 7.39 | 7.34 | 127.00 | 85.00 | 0.60 | 0.60 | 19.00 | 18.00 | 8.40 | 8.20 | 1.00 | 0.95 | 75.00 | 46.00 | 86.00 | 50.00 | 87.00 | 50.00 | 150.00 | 107.00 | 24.00 | 23.00 | 101.00 | 69.00 | 11.40 | 10.90 | 34.50 | 33.20 | 1.30 | 1.21 | 1.90 | 1.50 | 101.00 | 62.00 | 102.00 | 64.00 | 102.00 | 64.00 | 272.67 | 205.00 | 196.00 | 187.00 | 4.20 | 3.90 | 26.00 | 13.00 | 139.00 | 138.00 | 100.00 | 92.00 | 151.00 | 92.00 | 146.00 | 96.00 | 146.00 | 96.00 | 37.11 | 36.40 | 11.00 | 10.10 | 0.00 | 0.00 | 65,665.00 | NaN | 0.50 | 4.00 | 6.00 | 0.00 | 5.00 | NaN | 133.00 | 3.10 | 3.10 | 42.10 | 41.00 | 7.35 | 7.34 | 120.00 | 107.00 | 0.60 | 0.60 | 18.00 | 18.00 | 8.30 | 8.30 | 1.01 | 1.01 | 66.00 | 55.00 | 74.00 | 62.00 | 74.00 | 62.00 | 140.00 | 134.00 | 23.00 | 23.00 | 90.00 | 82.00 | 11.10 | 11.00 | 33.50 | 33.00 | 1.30 | 1.21 | 2.05 | 2.00 | 90.00 | 74.00 | 90.00 | 78.00 | 90.00 | 79.00 | 223.33 | 214.00 | 181.00 | 181.00 | 4.10 | 4.10 | 21.00 | 16.00 | 139.00 | 138.00 | 99.00 | 96.00 | 136.00 | 112.00 | 131.00 | 115.00 | 130.00 | 115.00 | 36.70 | 36.60 | 12.12 | 12.10 | 104.00 | 170.10 | 33.20 | 0.00 | NaN | 0.00 | 109.00 | NaN | NaN | 504.00 | NaN | NaN | 0.00 | 0.00 | 0.00 | 0.00 | 67.00 | 40.00 | 40.00 | 103.50 | 65,413.00 | 7.36 | 0.14 | NaN | 0.00 | 28.00 | 138.00 | 0.00 | 36.50 | 1,386.20 | 0.00 | 10.40 | 80.30 |
| 75% | 75.00 | 0.00 | 3.40 | NaN | 301.00 | NaN | 703.03 | 0.13 | 0.06 | 0.00 | 0.00 | 1.10 | 32.93 | 32.00 | 0.00 | 1.53 | 3.40 | 3.40 | 50.00 | 43.00 | 7.44 | 7.40 | 206.00 | 116.00 | 1.10 | 1.00 | 31.00 | 29.00 | 8.80 | 8.70 | 1.50 | 1.40 | 88.00 | 54.00 | 99.00 | 58.00 | 99.00 | 58.00 | 201.00 | 131.00 | 27.00 | 26.00 | 116.00 | 81.00 | 13.00 | 12.60 | 39.00 | 38.00 | 1.60 | 1.50 | 3.30 | 2.30 | 118.00 | 72.00 | 116.00 | 75.00 | 116.00 | 75.00 | 365.00 | 300.00 | 251.00 | 242.00 | 4.60 | 4.30 | 32.00 | 16.00 | 142.00 | 141.00 | 100.00 | 95.00 | 170.00 | 107.00 | 164.00 | 110.00 | 164.00 | 110.00 | 37.60 | 36.66 | 15.20 | 13.73 | 0.00 | 0.00 | 98,342.00 | NaN | 0.85 | 4.00 | 6.00 | 0.00 | 5.00 | NaN | 196.00 | 3.60 | 3.60 | 49.20 | 48.00 | 7.41 | 7.40 | 216.00 | 178.00 | 1.10 | 1.10 | 31.00 | 31.00 | 8.80 | 8.80 | 1.55 | 1.55 | 77.00 | 65.00 | 86.00 | 73.00 | 87.00 | 74.00 | 189.00 | 179.00 | 25.10 | 25.00 | 106.00 | 97.00 | 12.80 | 12.70 | 38.40 | 38.10 | 1.60 | 1.50 | 3.60 | 3.60 | 104.00 | 88.00 | 104.00 | 92.00 | 104.00 | 92.00 | 328.00 | 317.48 | 241.00 | 240.00 | 4.60 | 4.50 | 26.00 | 20.00 | 141.00 | 141.00 | 100.00 | 99.00 | 156.00 | 133.00 | 150.00 | 134.00 | 150.00 | 134.00 | 37.00 | 36.94 | 16.80 | 16.70 | 120.00 | 177.80 | 37.90 | 0.00 | NaN | 0.00 | 161.00 | NaN | NaN | 679.00 | NaN | NaN | 0.00 | 0.00 | 0.00 | 0.00 | 125.00 | 47.00 | 47.00 | 153.00 | 98,298.00 | 7.42 | 0.41 | NaN | 0.00 | 36.00 | 141.00 | 0.00 | 36.70 | 2,324.55 | 1.00 | 15.10 | 97.10 |
| max | 89.00 | 1.00 | 4.60 | NaN | 308.00 | NaN | 2,201.05 | 0.99 | 0.97 | 1.00 | 1.00 | 51.00 | 67.81 | 127.00 | 1.00 | 11.18 | 4.60 | 4.50 | 111.00 | 85.91 | 7.62 | 7.56 | 540.87 | 448.89 | 51.00 | 51.00 | 126.00 | 113.09 | 10.80 | 10.30 | 11.11 | 9.94 | 181.00 | 89.00 | 165.00 | 90.00 | 165.00 | 90.00 | 611.00 | 288.00 | 40.00 | 39.00 | 177.00 | 175.00 | 17.20 | 16.70 | 51.50 | 50.00 | 7.76 | 6.13 | 19.80 | 15.10 | 322.00 | 119.00 | 184.00 | 112.00 | 181.00 | 112.00 | 834.80 | 604.23 | 585.00 | 557.45 | 7.00 | 5.80 | 92.00 | 100.00 | 158.00 | 153.00 | 100.00 | 100.00 | 295.00 | 172.00 | 232.00 | 160.00 | 232.00 | 160.00 | 39.90 | 37.80 | 46.08 | 40.90 | 1.00 | 1.00 | 131,051.00 | NaN | 1.00 | 4.00 | 6.00 | 1.00 | 5.00 | NaN | 598.70 | 4.70 | 4.70 | 111.50 | 107.00 | 7.57 | 7.56 | 534.90 | 514.90 | 40.40 | 40.40 | 135.00 | 135.00 | 11.40 | 11.31 | 11.60 | 11.57 | 135.00 | 104.00 | 143.00 | 113.00 | 144.00 | 114.00 | 695.04 | 670.00 | 39.00 | 39.00 | 164.00 | 144.00 | 17.40 | 17.30 | 51.70 | 51.50 | 7.76 | 6.13 | 18.10 | 18.02 | 293.38 | 140.00 | 165.00 | 138.00 | 163.00 | 138.00 | 720.00 | 654.81 | 585.00 | 585.00 | 7.20 | 7.10 | 59.00 | 189.00 | 157.00 | 157.00 | 100.00 | 100.00 | 246.00 | 198.00 | 223.00 | 194.00 | 223.00 | 195.00 | 39.50 | 39.30 | 44.10 | 44.10 | 178.00 | 195.59 | 51.40 | 1.00 | NaN | 1.00 | 204.00 | NaN | NaN | 927.00 | NaN | NaN | 1.00 | 1.00 | 1.00 | 1.00 | 200.00 | 95.00 | 95.00 | 498.00 | 131,051.00 | 7.59 | 159.09 | NaN | 0.00 | 60.00 | 158.00 | 1.00 | 39.70 | 8,716.67 | 1.00 | 45.80 | 186.00 |
train_stat2.T.head(200)
| Category | Unit of Measure | Data Type | Description | Example | count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| age | demographic | Years | numeric | The age of the patient on unit admission | None | 87,485.00 | 62.31 | 16.78 | 16.00 | 52.00 | 65.00 | 75.00 | 89.00 |
| aids | APACHE comorbidity | None | binary | Whether the patient has a definitive diagnosis of acquired immune deficiency syndrome (AIDS) (not HIV positive alone) | 1 | 90,998.00 | 0.00 | 0.03 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| albumin_apache | APACHE covariate | g/L | numeric | The albumin concentration measured during the first 24 hours which results in the highest APACHE III score | 30 | 37,334.00 | 2.90 | 0.68 | 1.20 | 2.40 | 2.90 | 3.40 | 4.60 |
| apache_2_bodysystem | APACHE grouping | None | string | Admission diagnosis group for APACHE II | Respiratory | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| apache_2_diagnosis | APACHE covariate | None | string | The APACHE II diagnosis for the ICU admission | 308 | 90,051.00 | 185.40 | 86.05 | 101.00 | 113.00 | 122.00 | 301.00 | 308.00 |
| apache_3j_bodysystem | APACHE grouping | None | string | Admission diagnosis group for APACHE III | Cardiovascular | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| apache_3j_diagnosis | APACHE covariate | None | string | The APACHE III-J sub-diagnosis code which best describes the reason for the ICU admission | 1405 | 90,612.00 | 558.22 | 463.27 | 0.01 | 203.01 | 409.02 | 703.03 | 2,201.05 |
| apache_4a_hospital_death_prob | APACHE prediction | None | numeric | The APACHE IVa probabilistic prediction of in-hospital mortality for the patient which utilizes the APACHE III score and other covariates, including diagnosis. | 0.31 | 83,766.00 | 0.09 | 0.25 | -1.00 | 0.02 | 0.05 | 0.13 | 0.99 |
| apache_4a_icu_death_prob | APACHE prediction | None | numeric | The APACHE IVa probabilistic prediction of in ICU mortality for the patient which utilizes the APACHE III score and other covariates, including diagnosis | 0.24 | 83,766.00 | 0.04 | 0.22 | -1.00 | 0.01 | 0.02 | 0.06 | 0.97 |
| apache_post_operative | APACHE covariate | None | binary | The APACHE operative status; 1 for post-operative, 0 for non-operative | 1 | 91,713.00 | 0.20 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| arf_apache | APACHE covariate | None | binary | Whether the patient had acute renal failure during the first 24 hours of their unit stay, defined as a 24 hour urine output <410ml, creatinine >=133 micromol/L and no chronic dialysis | 0 | 90,998.00 | 0.03 | 0.16 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| bilirubin_apache | APACHE covariate | micromol/L | numeric | The bilirubin concentration measured during the first 24 hours which results in the highest APACHE III score | 20 | 33,579.00 | 1.15 | 2.17 | 0.10 | 0.40 | 0.60 | 1.10 | 51.00 |
| bmi | demographic | kilograms/metres^2 | string | The body mass index of the person on unit admission | 21.5 | 88,284.00 | 29.19 | 8.28 | 14.84 | 23.64 | 27.65 | 32.93 | 67.81 |
| bun_apache | APACHE covariate | mmol/L | numeric | The blood urea nitrogen concentration measured during the first 24 hours which results in the highest APACHE III score | None | 72,451.00 | 25.83 | 20.67 | 4.00 | 13.00 | 19.00 | 32.00 | 127.00 |
| cirrhosis | APACHE comorbidity | None | binary | Whether the patient has a history of heavy alcohol use with portal hypertension and varices, other causes of cirrhosis with evidence of portal hypertension and varices, or biopsy proven cirrhosis. This comorbidity does not apply to patients with a functioning liver transplant. | 1 | 90,998.00 | 0.02 | 0.12 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| creatinine_apache | APACHE covariate | micromol/L | numeric | The creatinine concentration measured during the first 24 hours which results in the highest APACHE III score | 70 | 72,860.00 | 1.48 | 1.53 | 0.30 | 0.72 | 0.98 | 1.53 | 11.18 |
| d1_albumin_max | labs | None | numeric | The lowest albumin concentration of the patient in their serum during the first 24 hours of their unit stay | 30 | 42,617.00 | 2.97 | 0.67 | 1.20 | 2.50 | 3.00 | 3.40 | 4.60 |
| d1_albumin_min | labs | g/L | numeric | The lowest albumin concentration of the patient in their serum during the first 24 hours of their unit stay | 30 | 42,617.00 | 2.90 | 0.67 | 1.10 | 2.40 | 2.90 | 3.40 | 4.50 |
| d1_arterial_pco2_max | labs blood gas | Millimetres of mercury | numeric | The highest arterial partial pressure of carbon dioxide for the patient during the first 24 hours of their unit stay | 40 | 32,442.00 | 45.25 | 14.67 | 18.40 | 36.00 | 42.80 | 50.00 | 111.00 |
| d1_arterial_pco2_min | labs blood gas | Millimetres of mercury | numeric | The lowest arterial partial pressure of carbon dioxide for the patient during the first 24 hours of their unit stay | 40 | 32,442.00 | 38.43 | 10.94 | 14.90 | 32.00 | 37.00 | 43.00 | 85.91 |
| d1_arterial_ph_max | labs blood gas | None | numeric | The highest arterial pH for the patient during the first 24 hours of their unit stay | 7.4 | 31,590.00 | 7.39 | 0.08 | 7.05 | 7.34 | 7.39 | 7.44 | 7.62 |
| d1_arterial_ph_min | labs blood gas | None | numeric | The lowest arterial pH for the patient during the first 24 hours of their unit stay | 7.4 | 31,590.00 | 7.32 | 0.11 | 6.89 | 7.27 | 7.34 | 7.40 | 7.56 |
| d1_arterial_po2_max | labs blood gas | Millimetres of mercury | numeric | The highest arterial partial pressure of oxygen for the patient during the first 24 hours of their unit stay | 80 | 32,451.00 | 165.91 | 108.01 | 39.00 | 88.10 | 127.00 | 206.00 | 540.87 |
| d1_arterial_po2_min | labs blood gas | Millimetres of mercury | numeric | The lowest arterial partial pressure of oxygen for the patient during the first 24 hours of their unit stay | 80 | 32,451.00 | 103.51 | 61.85 | 28.00 | 69.00 | 85.00 | 116.00 | 448.89 |
| d1_bilirubin_max | labs | micromol/L | numeric | The highest bilirubin concentration of the patient in their serum or plasma during the first 24 hours of their unit stay | 20 | 38,040.00 | 1.14 | 2.13 | 0.20 | 0.40 | 0.60 | 1.10 | 51.00 |
| d1_bilirubin_min | labs | micromol/L | numeric | The lowest bilirubin concentration of the patient in their serum or plasma during the first 24 hours of their unit stay | 20 | 38,040.00 | 1.07 | 2.02 | 0.20 | 0.40 | 0.60 | 1.00 | 51.00 |
| d1_bun_max | labs | mmol/L | numeric | The highest blood urea nitrogen concentration of the patient in their serum or plasma during the first 24 hours of their unit stay | 5 | 81,199.00 | 25.69 | 20.47 | 4.00 | 13.00 | 19.00 | 31.00 | 126.00 |
| d1_bun_min | labs | mmol/L | numeric | The lowest blood urea nitrogen concentration of the patient in their serum or plasma during the first 24 hours of their unit stay | 5 | 81,199.00 | 23.77 | 18.80 | 3.00 | 12.00 | 18.00 | 29.00 | 113.09 |
| d1_calcium_max | labs | mmol/L | numeric | The highest calcium concentration of the patient in their serum during the first 24 hours of their unit stay | 2.5 | 78,644.00 | 8.38 | 0.74 | 6.20 | 7.90 | 8.40 | 8.80 | 10.80 |
| d1_calcium_min | labs | mmol/L | numeric | The lowest calcium concentration of the patient in their serum during the first 24 hours of their unit stay | 2.5 | 78,644.00 | 8.18 | 0.78 | 5.50 | 7.70 | 8.20 | 8.70 | 10.30 |
| d1_creatinine_max | labs | micromol/L | numeric | The highest creatinine concentration of the patient in their serum or plasma during the first 24 hours of their unit stay | 70 | 81,544.00 | 1.49 | 1.51 | 0.34 | 0.76 | 1.00 | 1.50 | 11.11 |
| d1_creatinine_min | labs | micromol/L | numeric | The lowest creatinine concentration of the patient in their serum or plasma during the first 24 hours of their unit stay | 70 | 81,544.00 | 1.37 | 1.33 | 0.30 | 0.71 | 0.95 | 1.40 | 9.94 |
| d1_diasbp_invasive_max | vitals | Millimetres of mercury | numeric | The patient's highest diastolic blood pressure during the first 24 hours of their unit stay, invasively measured | 60 | 23,729.00 | 78.76 | 21.73 | 37.00 | 65.00 | 75.00 | 88.00 | 181.00 |
| d1_diasbp_invasive_min | vitals | Millimetres of mercury | numeric | The patient's lowest diastolic blood pressure during the first 24 hours of their unit stay, invasively measured | 60 | 23,729.00 | 46.74 | 12.86 | 5.00 | 39.00 | 46.00 | 54.00 | 89.00 |
| d1_diasbp_max | vitals | Millimetres of mercury | numeric | The patient's highest diastolic blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured | 60 | 91,548.00 | 88.49 | 19.80 | 46.00 | 75.00 | 86.00 | 99.00 | 165.00 |
| d1_diasbp_min | vitals | Millimetres of mercury | numeric | The patient's lowest diastolic blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured | 60 | 91,548.00 | 50.16 | 13.32 | 13.00 | 42.00 | 50.00 | 58.00 | 90.00 |
| d1_diasbp_noninvasive_max | vitals | Millimetres of mercury | numeric | The patient's highest diastolic blood pressure during the first 24 hours of their unit stay, non-invasively measured | 60 | 90,673.00 | 88.61 | 19.79 | 46.00 | 75.00 | 87.00 | 99.00 | 165.00 |
| d1_diasbp_noninvasive_min | vitals | Millimetres of mercury | numeric | The patient's lowest diastolic blood pressure during the first 24 hours of their unit stay, non-invasively measured | 60 | 90,673.00 | 50.24 | 13.34 | 13.00 | 42.00 | 50.00 | 58.00 | 90.00 |
| d1_glucose_max | labs | mmol/L | numeric | The highest glucose concentration of the patient in their serum or plasma during the first 24 hours of their unit stay | 5 | 85,906.00 | 174.64 | 86.69 | 73.00 | 117.00 | 150.00 | 201.00 | 611.00 |
| d1_glucose_min | labs | mmol/L | numeric | The lowest glucose concentration of the patient in their serum or plasma during the first 24 hours of their unit stay | 5 | 85,906.00 | 114.38 | 38.27 | 33.00 | 91.00 | 107.00 | 131.00 | 288.00 |
| d1_hco3_max | labs | mmol/L | numeric | The highest bicarbonate concentration for the patient in their serum or plasma during the first 24 hours of their unit stay | 30 | 76,642.00 | 24.37 | 4.37 | 12.00 | 22.00 | 24.00 | 27.00 | 40.00 |
| d1_hco3_min | labs | None | numeric | The lowest bicarbonate concentration for the patient in their serum or plasma during the first 24 hours of their unit stay | 30 | 76,642.00 | 23.17 | 4.99 | 7.00 | 21.00 | 23.00 | 26.00 | 39.00 |
| d1_heartrate_max | vitals | Beats per minute | numeric | The patient's highest heart rate during the first 24 hours of their unit stay | 75 | 91,568.00 | 103.00 | 22.02 | 58.00 | 87.00 | 101.00 | 116.00 | 177.00 |
| d1_heartrate_min | vitals | Beats per minute | numeric | The patient's lowest heart rate during the first 24 hours of their unit stay | 75 | 91,568.00 | 70.32 | 17.12 | 0.00 | 60.00 | 69.00 | 81.00 | 175.00 |
| d1_hemaglobin_max | labs | g/dL | numeric | The highest hemoglobin concentration for the patient during the first 24 hours of their unit stay | 10 | 79,566.00 | 11.45 | 2.17 | 6.80 | 9.80 | 11.40 | 13.00 | 17.20 |
| d1_hemaglobin_min | labs | g/dL | numeric | The lowest hemoglobin concentration for the patient during the first 24 hours of their unit stay | 10 | 79,566.00 | 10.89 | 2.36 | 5.30 | 9.20 | 10.90 | 12.60 | 16.70 |
| d1_hematocrit_max | labs | Fraction | numeric | The highest volume proportion of red blood cells in a patient's blood during the first 24 hours of their unit stay, expressed as a fraction | 0.4 | 80,059.00 | 34.53 | 6.24 | 20.40 | 30.00 | 34.50 | 39.00 | 51.50 |
| d1_hematocrit_min | labs | Fraction | numeric | The lowest volume proportion of red blood cells in a patient's blood during the first 24 hours of their unit stay, expressed as a fraction | 0.4 | 80,059.00 | 32.95 | 6.85 | 16.10 | 28.00 | 33.20 | 38.00 | 50.00 |
| d1_inr_max | labs | micromol/L | numeric | The highest international normalized ratio for the patient during the first 24 hours of their unit stay | 1 | 33,772.00 | 1.60 | 0.96 | 0.90 | 1.10 | 1.30 | 1.60 | 7.76 |
| d1_inr_min | labs | micromol/L | numeric | The lowest international normalized ratio for the patient during the first 24 hours of their unit stay | 1 | 33,772.00 | 1.48 | 0.75 | 0.90 | 1.10 | 1.21 | 1.50 | 6.13 |
| d1_lactate_max | labs | mmol/L | numeric | The highest lactate concentration for the patient in their serum or plasma during the first 24 hours of their unit stay | 1 | 23,317.00 | 2.93 | 3.08 | 0.40 | 1.20 | 1.90 | 3.30 | 19.80 |
| d1_lactate_min | labs | mmol/L | numeric | The lowest lactate concentration for the patient in their serum or plasma during the first 24 hours of their unit stay | 1 | 23,317.00 | 2.13 | 2.11 | 0.40 | 1.00 | 1.50 | 2.30 | 15.10 |
| d1_mbp_invasive_max | vitals | Millimetres of mercury | numeric | The patient's highest mean blood pressure during the first 24 hours of their unit stay, invasively measured | 80 | 23,936.00 | 114.89 | 49.45 | 38.00 | 89.00 | 101.00 | 118.00 | 322.00 |
| d1_mbp_invasive_min | vitals | Millimetres of mercury | numeric | The patient's lowest mean blood pressure during the first 24 hours of their unit stay, invasively measured | 80 | 23,936.00 | 62.32 | 18.06 | 2.00 | 54.00 | 62.00 | 72.00 | 119.00 |
| d1_mbp_max | vitals | Millimetres of mercury | numeric | The patient's highest mean blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured | 80 | 91,493.00 | 104.65 | 20.81 | 60.00 | 90.00 | 102.00 | 116.00 | 184.00 |
| d1_mbp_min | vitals | Millimetres of mercury | numeric | The patient's lowest mean blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured | 80 | 91,493.00 | 64.87 | 15.68 | 22.00 | 55.00 | 64.00 | 75.00 | 112.00 |
| d1_mbp_noninvasive_max | vitals | Millimetres of mercury | numeric | The patient's highest mean blood pressure during the first 24 hours of their unit stay, non-invasively measured | 80 | 90,234.00 | 104.59 | 20.70 | 60.00 | 90.00 | 102.00 | 116.00 | 181.00 |
| d1_mbp_noninvasive_min | vitals | Millimetres of mercury | numeric | The patient's lowest mean blood pressure during the first 24 hours of their unit stay, non-invasively measured | 80 | 90,234.00 | 64.94 | 15.70 | 22.00 | 55.00 | 64.00 | 75.00 | 112.00 |
| d1_pao2fio2ratio_max | labs blood gas | Fraction | numeric | The highest fraction of inspired oxygen for the patient during the first 24 hours of their unit stay | 0.21 | 25,705.00 | 285.67 | 128.22 | 54.80 | 192.29 | 272.67 | 365.00 | 834.80 |
| d1_pao2fio2ratio_min | labs blood gas | Fraction | numeric | The lowest fraction of inspired oxygen for the patient during the first 24 hours of their unit stay | 0.21 | 25,705.00 | 223.52 | 117.55 | 36.00 | 132.50 | 205.00 | 300.00 | 604.23 |
| d1_platelets_max | labs | 10^9/L | numeric | The highest platelet count for the patient during the first 24 hours of their unit stay | 200 | 78,269.00 | 207.11 | 89.63 | 27.00 | 148.00 | 196.00 | 251.00 | 585.00 |
| d1_platelets_min | labs | 10^9/L | numeric | The lowest platelet count for the patient during the first 24 hours of their unit stay | 200 | 78,269.00 | 196.77 | 88.18 | 18.55 | 138.00 | 187.00 | 242.00 | 557.45 |
| d1_potassium_max | labs | mmol/L | numeric | The highest potassium concentration for the patient in their serum or plasma during the first 24 hours of their unit stay | 3.8 | 82,128.00 | 4.25 | 0.67 | 2.80 | 3.80 | 4.20 | 4.60 | 7.00 |
| d1_potassium_min | labs | mmol/L | numeric | The lowest potassium concentration for the patient in their serum or plasma during the first 24 hours of their unit stay | 3.8 | 82,128.00 | 3.93 | 0.58 | 2.40 | 3.60 | 3.90 | 4.30 | 5.80 |
| d1_resprate_max | vitals | Breaths per minute | numeric | The patient's highest respiratory rate during the first 24 hours of their unit stay | 14 | 91,328.00 | 28.88 | 10.70 | 14.00 | 22.00 | 26.00 | 32.00 | 92.00 |
| d1_resprate_min | vitals | Breaths per minute | numeric | The patient's lowest respiratory rate during the first 24 hours of their unit stay | 14 | 91,328.00 | 12.85 | 5.06 | 0.00 | 10.00 | 13.00 | 16.00 | 100.00 |
| d1_sodium_max | labs | mmol/L | numeric | The highest sodium concentration for the patient in their serum or plasma during the first 24 hours of their unit stay | 145 | 81,518.00 | 139.12 | 4.82 | 123.00 | 137.00 | 139.00 | 142.00 | 158.00 |
| d1_sodium_min | labs | mmol/L | numeric | The lowest sodium concentration for the patient in their serum or plasma during the first 24 hours of their unit stay | 145 | 81,518.00 | 137.72 | 4.92 | 117.00 | 135.00 | 138.00 | 141.00 | 153.00 |
| d1_spo2_max | vitals | Percentage | numeric | The patient's highest peripheral oxygen saturation during the first 24 hours of their unit stay | None | 91,380.00 | 99.24 | 1.79 | 0.00 | 99.00 | 100.00 | 100.00 | 100.00 |
| d1_spo2_min | vitals | Percentage | numeric | The patient's lowest peripheral oxygen saturation during the first 24 hours of their unit stay | 100 | 91,380.00 | 90.45 | 10.03 | 0.00 | 89.00 | 92.00 | 95.00 | 100.00 |
| d1_sysbp_invasive_max | vitals | Millimetres of mercury | numeric | The patient's highest systolic blood pressure during the first 24 hours of their unit stay, invasively measured | 120 | 23,754.00 | 154.27 | 32.29 | 71.00 | 134.00 | 151.00 | 170.00 | 295.00 |
| d1_sysbp_invasive_min | vitals | Millimetres of mercury | numeric | The patient's lowest systolic blood pressure during the first 24 hours of their unit stay, invasively measured | 120 | 23,754.00 | 93.81 | 24.98 | 10.00 | 80.00 | 92.00 | 107.00 | 172.00 |
| d1_sysbp_max | vitals | Millimetres of mercury | numeric | The patient's highest systolic blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured | 120 | 91,554.00 | 148.34 | 25.73 | 90.00 | 130.00 | 146.00 | 164.00 | 232.00 |
| d1_sysbp_min | vitals | Millimetres of mercury | numeric | The patient's lowest systolic blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured | 120 | 91,554.00 | 96.92 | 20.68 | 41.00 | 83.00 | 96.00 | 110.00 | 160.00 |
| d1_sysbp_noninvasive_max | vitals | Millimetres of mercury | numeric | The patient's highest systolic blood pressure during the first 24 hours of their unit stay, non-invasively measured | 120 | 90,686.00 | 148.24 | 25.79 | 90.00 | 130.00 | 146.00 | 164.00 | 232.00 |
| d1_sysbp_noninvasive_min | vitals | Millimetres of mercury | numeric | The patient's lowest systolic blood pressure during the first 24 hours of their unit stay, non-invasively measured | 120 | 90,686.00 | 96.99 | 20.71 | 41.03 | 84.00 | 96.00 | 110.00 | 160.00 |
| d1_temp_max | vitals | Degrees Celsius | numeric | The patient's highest core temperature during the first 24 hours of their unit stay, invasively measured | 33 | 89,389.00 | 37.28 | 0.69 | 35.10 | 36.90 | 37.11 | 37.60 | 39.90 |
| d1_temp_min | vitals | Degrees Celsius | numeric | The patient's lowest core temperature during the first 24 hours of their unit stay | 33 | 89,389.00 | 36.27 | 0.75 | 31.89 | 36.10 | 36.40 | 36.66 | 37.80 |
| d1_wbc_max | labs | 10^9/L | numeric | The highest white blood cell count for the patient during the first 24 hours of their unit stay | 10 | 78,539.00 | 12.48 | 6.80 | 1.20 | 8.00 | 11.00 | 15.20 | 46.08 |
| d1_wbc_min | labs | 10^9/L | numeric | The lowest white blood cell count for the patient during the first 24 hours of their unit stay | 10 | 78,539.00 | 11.31 | 5.95 | 0.90 | 7.40 | 10.10 | 13.73 | 40.90 |
| diabetes_mellitus | APACHE comorbidity | None | binary | Whether the patient has been diagnosed with diabetes, either juvenile or adult onset, which requires medication. | 1 | 90,998.00 | 0.23 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| elective_surgery | demographic | None | binary | Whether the patient was admitted to the hospital for an elective surgical operation | 0 | 91,713.00 | 0.18 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| encounter_id | identifier | None | integer | Unique identifier associated with a patient unit stay | None | 91,713.00 | 65,606.08 | 37,795.09 | 1.00 | 32,852.00 | 65,665.00 | 98,342.00 | 131,051.00 |
| ethnicity | demographic | None | string | The common national or cultural tradition which the person belongs to | Caucasian | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| fio2_apache | APACHE covariate | Fraction | numeric | The fraction of inspired oxygen from the arterial blood gas taken during the first 24 hours of unit admission which produces the highest APACHE III score for oxygenation | 0.21 | 20,845.00 | 0.60 | 0.26 | 0.21 | 0.40 | 0.50 | 0.85 | 1.00 |
| gcs_eyes_apache | APACHE covariate | None | integer | The eye opening component of the Glasgow Coma Scale measured during the first 24 hours which results in the highest APACHE III score | 4 | 89,812.00 | 3.47 | 0.95 | 1.00 | 3.00 | 4.00 | 4.00 | 4.00 |
| gcs_motor_apache | APACHE covariate | None | integer | The motor component of the Glasgow Coma Scale measured during the first 24 hours which results in the highest APACHE III score | 6 | 89,812.00 | 5.47 | 1.29 | 1.00 | 6.00 | 6.00 | 6.00 | 6.00 |
| gcs_unable_apache | APACHE covariate | None | binary | Whether the Glasgow Coma Scale was unable to be assessed due to patient sedation | 1 | 90,676.00 | 0.01 | 0.10 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| gcs_verbal_apache | APACHE covariate | None | integer | The verbal component of the Glasgow Coma Scale measured during the first 24 hours which results in the highest APACHE III score | 5 | 89,812.00 | 3.99 | 1.56 | 1.00 | 4.00 | 5.00 | 5.00 | 5.00 |
| gender | demographic | None | string | The genotypical sex of the patient | F | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| glucose_apache | APACHE covariate | mmol/L | numeric | The glucose concentration measured during the first 24 hours which results in the highest APACHE III score | 5 | 80,677.00 | 160.33 | 90.79 | 39.00 | 97.00 | 133.00 | 196.00 | 598.70 |
| h1_albumin_max | labs | None | numeric | The lowest albumin concentration of the patient in their serum during the first hour of their unit stay | 30 | 7,889.00 | 3.03 | 0.73 | 1.10 | 2.50 | 3.10 | 3.60 | 4.70 |
| h1_albumin_min | labs | g/L | numeric | The lowest albumin concentration of the patient in their serum during the first hour of their unit stay | 30 | 7,889.00 | 3.03 | 0.73 | 1.10 | 2.50 | 3.10 | 3.60 | 4.70 |
| h1_arterial_pco2_max | labs blood gas | Millimetres of mercury | numeric | The highest arterial partial pressure of carbon dioxide for the patient during the first hour of their unit stay | 40 | 15,754.00 | 44.67 | 14.63 | 15.00 | 36.00 | 42.10 | 49.20 | 111.50 |
| h1_arterial_pco2_min | labs blood gas | Millimetres of mercury | numeric | The lowest arterial partial pressure of carbon dioxide for the patient during the first hour of their unit stay | 40 | 15,754.00 | 43.38 | 14.11 | 15.00 | 35.00 | 41.00 | 48.00 | 107.00 |
| h1_arterial_ph_max | labs blood gas | None | numeric | The highest arterial pH for the patient during the first hour of their unit stay | 7.4 | 15,289.00 | 7.34 | 0.11 | 6.93 | 7.29 | 7.35 | 7.41 | 7.57 |
| h1_arterial_ph_min | labs blood gas | None | numeric | The lowest arterial pH for the patient during the first hour of their unit stay | 7.4 | 15,289.00 | 7.33 | 0.11 | 6.90 | 7.28 | 7.34 | 7.40 | 7.56 |
| h1_arterial_po2_max | labs blood gas | Millimetres of mercury | numeric | The highest arterial partial pressure of oxygen for the patient during the first hour of their unit stay | 80 | 15,768.00 | 163.84 | 113.46 | 34.00 | 80.70 | 120.00 | 216.00 | 534.90 |
| h1_arterial_po2_min | labs blood gas | Millimetres of mercury | numeric | The lowest arterial partial pressure of oxygen for the patient during the first hour of their unit stay | 80 | 15,768.00 | 144.15 | 98.46 | 31.00 | 77.00 | 107.00 | 178.00 | 514.90 |
| h1_bilirubin_max | labs | micromol/L | numeric | The highest bilirubin concentration of the patient in their serum or plasma during the first hour of their unit stay | 20 | 7,094.00 | 1.10 | 2.03 | 0.20 | 0.40 | 0.60 | 1.10 | 40.40 |
| h1_bilirubin_min | labs | micromol/L | numeric | The lowest bilirubin concentration of the patient in their serum or plasma during the first hour of their unit stay | 20 | 7,094.00 | 1.10 | 2.03 | 0.20 | 0.40 | 0.60 | 1.10 | 40.40 |
| h1_bun_max | labs | mmol/L | numeric | The highest blood urea nitrogen concentration of the patient in their serum or plasma during the first hour of their unit stay | 5 | 16,622.00 | 25.84 | 21.44 | 4.00 | 13.00 | 18.00 | 31.00 | 135.00 |
| h1_bun_min | labs | mmol/L | numeric | The lowest blood urea nitrogen concentration of the patient in their serum or plasma during the first hour of their unit stay | 5 | 16,622.00 | 25.82 | 21.42 | 4.00 | 13.00 | 18.00 | 31.00 | 135.00 |
| h1_calcium_max | labs | mmol/L | numeric | The highest calcium concentration of the patient in their serum during the first hour of their unit stay | 2.5 | 15,850.00 | 8.28 | 0.88 | 5.60 | 7.70 | 8.30 | 8.80 | 11.40 |
| h1_calcium_min | labs | mmol/L | numeric | The lowest calcium concentration of the patient in their serum during the first hour of their unit stay | 2.5 | 15,850.00 | 8.28 | 0.89 | 5.30 | 7.70 | 8.30 | 8.80 | 11.31 |
| h1_creatinine_max | labs | micromol/L | numeric | The highest creatinine concentration of the patient in their serum or plasma during the first hour of their unit stay | 70 | 16,756.00 | 1.53 | 1.58 | 0.33 | 0.79 | 1.01 | 1.55 | 11.60 |
| h1_creatinine_min | labs | micromol/L | numeric | The lowest creatinine concentration of the patient in their serum or plasma during the first hour of their unit stay | 70 | 16,756.00 | 1.53 | 1.57 | 0.33 | 0.79 | 1.01 | 1.55 | 11.57 |
| h1_diasbp_invasive_max | vitals | Millimetres of mercury | numeric | The patient's highest diastolic blood pressure during the first hour of their unit stay, invasively measured | 60 | 16,785.00 | 67.97 | 16.26 | 33.00 | 57.00 | 66.00 | 77.00 | 135.00 |
| h1_diasbp_invasive_min | vitals | Millimetres of mercury | numeric | The patient's lowest diastolic blood pressure during the first hour of their unit stay, invasively measured | 60 | 16,785.00 | 56.14 | 14.14 | 19.00 | 46.00 | 55.00 | 65.00 | 104.00 |
| h1_diasbp_max | vitals | Millimetres of mercury | numeric | The patient's highest diastolic blood pressure during the first hour of their unit stay, either non-invasively or invasively measured | 60 | 88,094.00 | 75.35 | 18.41 | 37.00 | 62.00 | 74.00 | 86.00 | 143.00 |
| h1_diasbp_min | vitals | Millimetres of mercury | numeric | The patient's lowest diastolic blood pressure during the first hour of their unit stay, either non-invasively or invasively measured | 60 | 88,094.00 | 62.84 | 16.36 | 22.00 | 52.00 | 62.00 | 73.00 | 113.00 |
| h1_diasbp_noninvasive_max | vitals | Millimetres of mercury | numeric | The patient's highest diastolic blood pressure during the first hour of their unit stay, non-invasively measured | 60 | 84,363.00 | 75.81 | 18.48 | 37.00 | 63.00 | 74.00 | 87.00 | 144.00 |
| h1_diasbp_noninvasive_min | vitals | Millimetres of mercury | numeric | The patient's lowest diastolic blood pressure during the first hour of their unit stay, non-invasively measured | 60 | 84,363.00 | 63.27 | 16.42 | 22.00 | 52.00 | 62.00 | 74.00 | 114.00 |
| h1_glucose_max | labs | mmol/L | numeric | The highest glucose concentration of the patient in their serum or plasma during the first hour of their unit stay | 5 | 39,099.00 | 167.99 | 94.72 | 59.00 | 111.00 | 140.00 | 189.00 | 695.04 |
| h1_glucose_min | labs | mmol/L | numeric | The lowest glucose concentration of the patient in their serum or plasma during the first hour of their unit stay | 5 | 39,099.00 | 159.22 | 89.16 | 42.00 | 106.00 | 134.00 | 179.00 | 670.00 |
| h1_hco3_max | labs | mmol/L | numeric | The highest bicarbonate concentration for the patient in their serum or plasma during the first hour of their unit stay | 30 | 15,619.00 | 22.50 | 5.21 | 6.00 | 20.00 | 23.00 | 25.10 | 39.00 |
| h1_hco3_min | labs | None | numeric | The lowest bicarbonate concentration for the patient in their serum or plasma during the first hour of their unit stay | 30 | 15,619.00 | 22.42 | 5.21 | 6.00 | 20.00 | 23.00 | 25.00 | 39.00 |
| h1_heartrate_max | vitals | Beats per minute | numeric | The patient's highest heart rate during the first hour of their unit stay | 75 | 88,923.00 | 92.23 | 21.82 | 46.00 | 77.00 | 90.00 | 106.00 | 164.00 |
| h1_heartrate_min | vitals | Beats per minute | numeric | The patient's lowest heart rate during the first hour of their unit stay | 75 | 88,923.00 | 83.66 | 20.28 | 36.00 | 69.00 | 82.00 | 97.00 | 144.00 |
| h1_hemaglobin_max | labs | g/dL | numeric | The highest hemoglobin concentration for the patient during the first hour of their unit stay | 10 | 18,590.00 | 11.19 | 2.37 | 5.10 | 9.50 | 11.10 | 12.80 | 17.40 |
| h1_hemaglobin_min | labs | g/dL | numeric | The lowest hemoglobin concentration for the patient during the first hour of their unit stay | 10 | 18,590.00 | 11.04 | 2.41 | 5.00 | 9.30 | 11.00 | 12.70 | 17.30 |
| h1_hematocrit_max | labs | Fraction | numeric | The highest volume proportion of red blood cells in a patient's blood during the first hour of their unit stay, expressed as a fraction | 0.4 | 18,293.00 | 33.67 | 6.84 | 16.00 | 28.90 | 33.50 | 38.40 | 51.70 |
| h1_hematocrit_min | labs | Fraction | numeric | The lowest volume proportion of red blood cells in a patient's blood during the first hour of their unit stay, expressed as a fraction | 0.4 | 18,293.00 | 33.22 | 7.03 | 15.50 | 28.10 | 33.00 | 38.10 | 51.50 |
| h1_inr_max | labs | micromol/L | numeric | The highest international normalized ratio for the patient during the first hour of their unit stay | 1 | 33,772.00 | 1.60 | 0.96 | 0.90 | 1.10 | 1.30 | 1.60 | 7.76 |
| h1_inr_min | labs | micromol/L | numeric | The lowest international normalized ratio for the patient during the first hour of their unit stay | 1 | 33,772.00 | 1.48 | 0.75 | 0.90 | 1.10 | 1.21 | 1.50 | 6.13 |
| h1_lactate_max | labs | mmol/L | numeric | The highest lactate concentration for the patient in their serum or plasma during the first hour of their unit stay | 1 | 7,344.00 | 3.07 | 2.93 | 0.40 | 1.30 | 2.05 | 3.60 | 18.10 |
| h1_lactate_min | labs | mmol/L | numeric | The lowest lactate concentration for the patient in their serum or plasma during the first hour of their unit stay | 1 | 7,344.00 | 3.02 | 2.88 | 0.40 | 1.30 | 2.00 | 3.60 | 18.02 |
| h1_mbp_invasive_max | vitals | Millimetres of mercury | numeric | The patient's highest mean blood pressure during the first hour of their unit stay, invasively measured | 80 | 16,869.00 | 94.88 | 30.81 | 35.62 | 78.00 | 90.00 | 104.00 | 293.38 |
| h1_mbp_invasive_min | vitals | Millimetres of mercury | numeric | The patient's lowest mean blood pressure during the first hour of their unit stay, invasively measured | 80 | 16,869.00 | 75.97 | 19.23 | 8.00 | 63.00 | 74.00 | 88.00 | 140.00 |
| h1_mbp_max | vitals | Millimetres of mercury | numeric | The patient's highest mean blood pressure during the first hour of their unit stay, either non-invasively or invasively measured | 80 | 87,074.00 | 91.61 | 20.53 | 49.00 | 77.00 | 90.00 | 104.00 | 165.00 |
| h1_mbp_min | vitals | Millimetres of mercury | numeric | The patient's lowest mean blood pressure during the first hour of their unit stay, either non-invasively or invasively measured | 80 | 87,074.00 | 79.40 | 19.13 | 32.00 | 66.00 | 78.00 | 92.00 | 138.00 |
| h1_mbp_noninvasive_max | vitals | Millimetres of mercury | numeric | The patient's highest mean blood pressure during the first hour of their unit stay, non-invasively measured | 80 | 82,629.00 | 91.59 | 20.55 | 49.00 | 77.00 | 90.00 | 104.00 | 163.00 |
| h1_mbp_noninvasive_min | vitals | Millimetres of mercury | numeric | The patient's lowest mean blood pressure during the first hour of their unit stay, non-invasively measured | 80 | 82,629.00 | 79.71 | 19.24 | 32.00 | 66.00 | 79.00 | 92.00 | 138.00 |
| h1_pao2fio2ratio_max | labs blood gas | Fraction | numeric | The highest fraction of inspired oxygen for the patient during the first hour of their unit stay | 0.21 | 11,518.00 | 244.40 | 129.96 | 42.00 | 142.00 | 223.33 | 328.00 | 720.00 |
| h1_pao2fio2ratio_min | labs blood gas | Fraction | numeric | The lowest fraction of inspired oxygen for the patient during the first hour of their unit stay | 0.21 | 11,518.00 | 235.93 | 126.46 | 38.00 | 136.00 | 214.00 | 317.48 | 654.81 |
| h1_platelets_max | labs | 10^9/L | numeric | The highest platelet count for the patient during the first hour of their unit stay | 200 | 16,040.00 | 196.10 | 92.65 | 20.00 | 133.00 | 181.00 | 241.00 | 585.00 |
| h1_platelets_min | labs | 10^9/L | numeric | The lowest platelet count for the patient during the first hour of their unit stay | 200 | 16,040.00 | 195.48 | 92.78 | 20.00 | 132.00 | 181.00 | 240.00 | 585.00 |
| h1_potassium_max | labs | mmol/L | numeric | The highest potassium concentration for the patient in their serum or plasma during the first hour of their unit stay | 3.8 | 19,611.00 | 4.20 | 0.76 | 2.50 | 3.70 | 4.10 | 4.60 | 7.20 |
| h1_potassium_min | labs | mmol/L | numeric | The lowest potassium concentration for the patient in their serum or plasma during the first hour of their unit stay | 3.8 | 19,611.00 | 4.15 | 0.75 | 2.50 | 3.70 | 4.10 | 4.50 | 7.10 |
| h1_resprate_max | vitals | Breaths per minute | numeric | The patient's highest respiratory rate during the first hour of their unit stay | 14 | 87,356.00 | 22.63 | 7.52 | 10.00 | 18.00 | 21.00 | 26.00 | 59.00 |
| h1_resprate_min | vitals | Breaths per minute | numeric | The patient's lowest respiratory rate during the first hour of their unit stay | 14 | 87,356.00 | 17.21 | 6.07 | 0.00 | 14.00 | 16.00 | 20.00 | 189.00 |
| h1_sodium_max | labs | mmol/L | numeric | The highest sodium concentration for the patient in their serum or plasma during the first hour of their unit stay | 145 | 19,096.00 | 138.24 | 5.75 | 114.00 | 136.00 | 139.00 | 141.00 | 157.00 |
| h1_sodium_min | labs | mmol/L | numeric | The lowest sodium concentration for the patient in their serum or plasma during the first hour of their unit stay | 145 | 19,096.00 | 137.90 | 5.68 | 114.00 | 135.00 | 138.00 | 141.00 | 157.00 |
| h1_spo2_max | vitals | Percentage | numeric | The patient's highest peripheral oxygen saturation during the first hour of their unit stay | None | 87,528.00 | 98.04 | 3.21 | 0.00 | 97.00 | 99.00 | 100.00 | 100.00 |
| h1_spo2_min | vitals | Percentage | numeric | The patient's lowest peripheral oxygen saturation during the first hour of their unit stay | 100 | 87,528.00 | 95.17 | 6.63 | 0.00 | 94.00 | 96.00 | 99.00 | 100.00 |
| h1_sysbp_invasive_max | vitals | Millimetres of mercury | numeric | The patient's highest systolic blood pressure during the first hour of their unit stay, invasively measured | 120 | 16,798.00 | 138.70 | 29.21 | 65.00 | 119.00 | 136.00 | 156.00 | 246.00 |
| h1_sysbp_invasive_min | vitals | Millimetres of mercury | numeric | The patient's lowest systolic blood pressure during the first hour of their unit stay, invasively measured | 120 | 16,798.00 | 114.83 | 27.97 | 31.44 | 95.00 | 112.00 | 133.00 | 198.00 |
| h1_sysbp_max | vitals | Millimetres of mercury | numeric | The patient's highest systolic blood pressure during the first hour of their unit stay, either non-invasively or invasively measured | 120 | 88,102.00 | 133.25 | 27.56 | 75.00 | 113.00 | 131.00 | 150.00 | 223.00 |
| h1_sysbp_min | vitals | Millimetres of mercury | numeric | The patient's lowest systolic blood pressure during the first hour of their unit stay, either non-invasively or invasively measured | 120 | 88,102.00 | 116.36 | 26.51 | 53.00 | 98.00 | 115.00 | 134.00 | 194.00 |
| h1_sysbp_noninvasive_max | vitals | Millimetres of mercury | numeric | The patient's highest systolic blood pressure during the first hour of their unit stay, non-invasively measured | 120 | 84,372.00 | 133.05 | 27.68 | 75.00 | 113.00 | 130.00 | 150.00 | 223.00 |
| h1_sysbp_noninvasive_min | vitals | Millimetres of mercury | numeric | The patient's lowest systolic blood pressure during the first hour of their unit stay, non-invasively measured | 120 | 84,372.00 | 116.55 | 26.62 | 53.00 | 98.00 | 115.00 | 134.00 | 195.00 |
| h1_temp_max | vitals | Degrees Celsius | numeric | The patient's highest core temperature during the first hour of their unit stay, invasively measured | 33 | 69,981.00 | 36.71 | 0.75 | 33.40 | 36.40 | 36.70 | 37.00 | 39.50 |
| h1_temp_min | vitals | Degrees Celsius | numeric | The patient's lowest core temperature during the first hour of their unit stay | 33 | 69,981.00 | 36.61 | 0.78 | 32.90 | 36.30 | 36.60 | 36.94 | 39.30 |
| h1_wbc_max | labs | 10^9/L | numeric | The highest white blood cell count for the patient during the first hour of their unit stay | 10 | 15,760.00 | 13.46 | 6.98 | 1.10 | 8.60 | 12.12 | 16.80 | 44.10 |
| h1_wbc_min | labs | 10^9/L | numeric | The lowest white blood cell count for the patient during the first hour of their unit stay | 10 | 15,760.00 | 13.42 | 6.97 | 1.09 | 8.60 | 12.10 | 16.70 | 44.10 |
| heart_rate_apache | APACHE covariate | Beats per minute | numeric | The heart rate measured during the first 24 hours which results in the highest APACHE III score | 75 | 90,835.00 | 99.71 | 30.87 | 30.00 | 86.00 | 104.00 | 120.00 | 178.00 |
| height | demographic | centimetres | numeric | The height of the person on unit admission | 180 | 90,379.00 | 169.64 | 10.80 | 137.20 | 162.50 | 170.10 | 177.80 | 195.59 |
| hematocrit_apache | APACHE covariate | Fraction | numeric | The hematocrit measured during the first 24 hours which results in the highest APACHE III score | 0.4 | 71,835.00 | 32.99 | 6.87 | 16.20 | 28.00 | 33.20 | 37.90 | 51.40 |
| hepatic_failure | APACHE comorbidity | None | binary | Whether the patient has cirrhosis and additional complications including jaundice and ascites, upper GI bleeding, hepatic encephalopathy, or coma. | 1 | 90,998.00 | 0.01 | 0.11 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| hospital_admit_source | demographic | None | string | The location of the patient prior to being admitted to the hospital | Home | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| hospital_death | demographic | None | binary | Whether the patient died during this hospitalization | 0 | 91,713.00 | 0.09 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| hospital_id | identifier | None | integer | Unique identifier associated with a hospital | None | 91,713.00 | 105.67 | 62.85 | 2.00 | 47.00 | 109.00 | 161.00 | 204.00 |
| icu_admit_source | demographic | None | string | The location of the patient prior to being admitted to the unit | Operating room | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| icu_admit_type | demographic | None | string | The type of unit admission for the patient | Cardiothoracic | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| icu_id | demographic | None | integer | A unique identifier for the unit to which the patient was admitted | None | 91,713.00 | 508.36 | 228.99 | 82.00 | 369.00 | 504.00 | 679.00 | 927.00 |
| icu_stay_type | demographic | None | string | NaN | None | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| icu_type | demographic | None | string | A classification which indicates the type of care the unit is capable of providing | Neurological ICU | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| immunosuppression | APACHE comorbidity | None | binary | Whether the patient has their immune system suppressed within six months prior to ICU admission for any of the following reasons; radiation therapy, chemotherapy, use of non-cytotoxic immunosuppressive drugs, high dose steroids (at least 0.3 mg/kg/day of methylprednisolone or equivalent for at least 6 months). | 1 | 90,998.00 | 0.03 | 0.16 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| intubated_apache | APACHE covariate | None | binary | Whether the patient was intubated at the time of the highest scoring arterial blood gas used in the oxygenation score | 0 | 90,998.00 | 0.15 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| leukemia | APACHE comorbidity | None | binary | Whether the patient has been diagnosed with acute or chronic myelogenous leukemia, acute or chronic lymphocytic leukemia, or multiple myeloma. | 1 | 90,998.00 | 0.01 | 0.08 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| lymphoma | APACHE comorbidity | None | binary | Whether the patient has been diagnosed with non-Hodgkin lymphoma. | 1 | 90,998.00 | 0.00 | 0.06 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| map_apache | APACHE covariate | Millimetres of mercury | numeric | The mean arterial pressure measured during the first 24 hours which results in the highest APACHE III score | None | 90,719.00 | 88.02 | 42.03 | 40.00 | 54.00 | 67.00 | 125.00 | 200.00 |
| paco2_apache | APACHE covariate | Millimetres of mercury | numeric | The partial pressure of carbon dioxide from the arterial blood gas taken during the first 24 hours of unit admission which produces the highest APACHE III score for oxygenation | 40 | 20,845.00 | 42.18 | 12.38 | 18.00 | 34.40 | 40.00 | 47.00 | 95.00 |
| paco2_for_ph_apache | APACHE covariate | Millimetres of mercury | numeric | The partial pressure of carbon dioxide from the arterial blood gas taken during the first 24 hours of unit admission which produces the highest APACHE III score for acid-base disturbance | 40 | 20,845.00 | 42.18 | 12.38 | 18.00 | 34.40 | 40.00 | 47.00 | 95.00 |
| pao2_apache | APACHE covariate | Millimetres of mercury | numeric | The partial pressure of oxygen from the arterial blood gas taken during the first 24 hours of unit admission which produces the highest APACHE III score for oxygenation | 80 | 20,845.00 | 131.15 | 83.61 | 31.00 | 77.50 | 103.50 | 153.00 | 498.00 |
| patient_id | identifier | None | integer | Unique identifier associated with a patient | None | 91,713.00 | 65,537.13 | 37,811.25 | 1.00 | 32,830.00 | 65,413.00 | 98,298.00 | 131,051.00 |
| ph_apache | APACHE covariate | None | numeric | The pH from the arterial blood gas taken during the first 24 hours of unit admission which produces the highest APACHE III score for acid-base disturbance | 7.4 | 20,845.00 | 7.35 | 0.10 | 6.96 | 7.31 | 7.36 | 7.42 | 7.59 |
| pre_icu_los_days | demographic | Days | numeric | The length of stay of the patient between hospital admission and unit admission | 3.5 | 91,713.00 | 0.84 | 2.49 | -24.95 | 0.04 | 0.14 | 0.41 | 159.09 |
| pred | GOSSIS example prediction | None | numeric | Example mortality prediction, shared as a 'baseline' based on one of the GOSSIS algorithm development models. | 0.000921 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| readmission_status | demographic | None | binary | Whether the current unit stay is the second (or greater) stay at an ICU within the same hospitalization | 0 | 91,713.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| resprate_apache | APACHE covariate | Breaths per minute | numeric | The respiratory rate measured during the first 24 hours which results in the highest APACHE III score | 14 | 90,479.00 | 25.81 | 15.11 | 4.00 | 11.00 | 28.00 | 36.00 | 60.00 |
| sodium_apache | APACHE covariate | mmol/L | numeric | The sodium concentration measured during the first 24 hours which results in the highest APACHE III score | 145 | 73,113.00 | 137.97 | 5.28 | 117.00 | 135.00 | 138.00 | 141.00 | 158.00 |
| solid_tumor_with_metastasis | APACHE comorbidity | None | binary | Whether the patient has been diagnosed with any solid tumor carcinoma (including malignant melanoma) which has evidence of metastasis. | 1 | 90,998.00 | 0.02 | 0.14 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| temp_apache | APACHE covariate | Degrees Celsius | numeric | The temperature measured during the first 24 hours which results in the highest APACHE III score | 33 | 87,605.00 | 36.41 | 0.83 | 32.10 | 36.20 | 36.50 | 36.70 | 39.70 |
| urineoutput_apache | APACHE covariate | Millilitres | numeric | The total urine output for the first 24 hours | 2000 | 42,715.00 | 1,738.28 | 1,448.16 | 0.00 | 740.36 | 1,386.20 | 2,324.55 | 8,716.67 |
| ventilated_apache | APACHE covariate | None | binary | Whether the patient was invasively ventilated at the time of the highest scoring arterial blood gas using the oxygenation scoring algorithm, including any mode of positive pressure ventilation delivered through a circuit attached to an endo-tracheal tube or tracheostomy | 1 | 90,998.00 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 |
| wbc_apache | APACHE covariate | 10^9/L | numeric | The white blood cell count measured during the first 24 hours which results in the highest APACHE III score | 10 | 69,701.00 | 12.13 | 6.92 | 0.90 | 7.50 | 10.40 | 15.10 | 45.80 |
| weight | demographic | kilograms | numeric | The weight (body mass) of the person on unit admission | 80 | 88,993.00 | 84.03 | 25.01 | 38.60 | 66.80 | 80.30 | 97.10 | 186.00 |
# Missing Values
train.isna().sum()
encounter_id 0 patient_id 0 hospital_id 0 hospital_death 0 age 4228 bmi 3429 elective_surgery 0 ethnicity 1395 gender 25 height 1334 hospital_admit_source 21409 icu_admit_source 112 icu_id 0 icu_stay_type 0 icu_type 0 pre_icu_los_days 0 readmission_status 0 weight 2720 albumin_apache 54379 apache_2_diagnosis 1662 apache_3j_diagnosis 1101 apache_post_operative 0 arf_apache 715 bilirubin_apache 58134 bun_apache 19262 creatinine_apache 18853 fio2_apache 70868 gcs_eyes_apache 1901 gcs_motor_apache 1901 gcs_unable_apache 1037 gcs_verbal_apache 1901 glucose_apache 11036 heart_rate_apache 878 hematocrit_apache 19878 intubated_apache 715 map_apache 994 paco2_apache 70868 paco2_for_ph_apache 70868 pao2_apache 70868 ph_apache 70868 resprate_apache 1234 sodium_apache 18600 temp_apache 4108 urineoutput_apache 48998 ventilated_apache 715 wbc_apache 22012 d1_diasbp_invasive_max 67984 d1_diasbp_invasive_min 67984 d1_diasbp_max 165 d1_diasbp_min 165 d1_diasbp_noninvasive_max 1040 d1_diasbp_noninvasive_min 1040 d1_heartrate_max 145 d1_heartrate_min 145 d1_mbp_invasive_max 67777 d1_mbp_invasive_min 67777 d1_mbp_max 220 d1_mbp_min 220 d1_mbp_noninvasive_max 1479 d1_mbp_noninvasive_min 1479 d1_resprate_max 385 d1_resprate_min 385 d1_spo2_max 333 d1_spo2_min 333 d1_sysbp_invasive_max 67959 d1_sysbp_invasive_min 67959 d1_sysbp_max 159 d1_sysbp_min 159 d1_sysbp_noninvasive_max 1027 d1_sysbp_noninvasive_min 1027 d1_temp_max 2324 d1_temp_min 2324 h1_diasbp_invasive_max 74928 h1_diasbp_invasive_min 74928 h1_diasbp_max 3619 h1_diasbp_min 3619 h1_diasbp_noninvasive_max 7350 h1_diasbp_noninvasive_min 7350 h1_heartrate_max 2790 h1_heartrate_min 2790 h1_mbp_invasive_max 74844 h1_mbp_invasive_min 74844 h1_mbp_max 4639 h1_mbp_min 4639 h1_mbp_noninvasive_max 9084 h1_mbp_noninvasive_min 9084 h1_resprate_max 4357 h1_resprate_min 4357 h1_spo2_max 4185 h1_spo2_min 4185 h1_sysbp_invasive_max 74915 h1_sysbp_invasive_min 74915 h1_sysbp_max 3611 h1_sysbp_min 3611 h1_sysbp_noninvasive_max 7341 h1_sysbp_noninvasive_min 7341 h1_temp_max 21732 h1_temp_min 21732 d1_albumin_max 49096 d1_albumin_min 49096 d1_bilirubin_max 53673 d1_bilirubin_min 53673 d1_bun_max 10514 d1_bun_min 10514 d1_calcium_max 13069 d1_calcium_min 13069 d1_creatinine_max 10169 d1_creatinine_min 10169 d1_glucose_max 5807 d1_glucose_min 5807 d1_hco3_max 15071 d1_hco3_min 15071 d1_hemaglobin_max 12147 d1_hemaglobin_min 12147 d1_hematocrit_max 11654 d1_hematocrit_min 11654 d1_inr_max 57941 d1_inr_min 57941 d1_lactate_max 68396 d1_lactate_min 68396 d1_platelets_max 13444 d1_platelets_min 13444 d1_potassium_max 9585 d1_potassium_min 9585 d1_sodium_max 10195 d1_sodium_min 10195 d1_wbc_max 13174 d1_wbc_min 13174 h1_albumin_max 83824 h1_albumin_min 83824 h1_bilirubin_max 84619 h1_bilirubin_min 84619 h1_bun_max 75091 h1_bun_min 75091 h1_calcium_max 75863 h1_calcium_min 75863 h1_creatinine_max 74957 h1_creatinine_min 74957 h1_glucose_max 52614 h1_glucose_min 52614 h1_hco3_max 76094 h1_hco3_min 76094 h1_hemaglobin_max 73123 h1_hemaglobin_min 73123 h1_hematocrit_max 73420 h1_hematocrit_min 73420 h1_inr_max 57941 h1_inr_min 57941 h1_lactate_max 84369 h1_lactate_min 84369 h1_platelets_max 75673 h1_platelets_min 75673 h1_potassium_max 72102 h1_potassium_min 72102 h1_sodium_max 72617 h1_sodium_min 72617 h1_wbc_max 75953 h1_wbc_min 75953 d1_arterial_pco2_max 59271 d1_arterial_pco2_min 59271 d1_arterial_ph_max 60123 d1_arterial_ph_min 60123 d1_arterial_po2_max 59262 d1_arterial_po2_min 59262 d1_pao2fio2ratio_max 66008 d1_pao2fio2ratio_min 66008 h1_arterial_pco2_max 75959 h1_arterial_pco2_min 75959 h1_arterial_ph_max 76424 h1_arterial_ph_min 76424 h1_arterial_po2_max 75945 h1_arterial_po2_min 75945 h1_pao2fio2ratio_max 80195 h1_pao2fio2ratio_min 80195 apache_4a_hospital_death_prob 7947 apache_4a_icu_death_prob 7947 aids 715 cirrhosis 715 diabetes_mellitus 715 hepatic_failure 715 immunosuppression 715 leukemia 715 lymphoma 715 solid_tumor_with_metastasis 715 apache_3j_bodysystem 1662 apache_2_bodysystem 1662 dtype: int64
# function to evaluate the score of our model
def eval_auc(pred,real):
false_positive_rate, recall, thresholds = roc_curve(real, pred)
roc_auc = auc(false_positive_rate, recall)
return roc_auc
# a wrapper class that we can have the same ouput whatever the model we choose
class Base_Model(object):
def __init__(self, train_df, test_df, features, categoricals=[], n_splits=5, verbose=True,ps={}):
self.train_df = train_df
self.test_df = test_df
self.features = features
self.n_splits = n_splits
self.categoricals = categoricals
self.target = 'hospital_death'
self.cv = self.get_cv()
self.verbose = verbose
# self.params = self.get_params()
self.params = self.set_params(ps)
self.y_pred, self.score, self.model , self.oof_pred = self.fit()
def train_model(self, train_set, val_set):
raise NotImplementedError
def get_cv(self):
cv = StratifiedKFold(n_splits=self.n_splits, shuffle=True, random_state=42)
return cv.split(self.train_df, self.train_df[self.target])
def get_params(self):
raise NotImplementedError
def convert_dataset(self, x_train, y_train, x_val, y_val):
raise NotImplementedError
def convert_x(self, x):
return x
def fit(self):
oof_pred = np.zeros((len(self.train_df), ))
y_pred = np.zeros((len(self.test_df), ))
for fold, (train_idx, val_idx) in enumerate(self.cv):
x_train, x_val = self.train_df[self.features].iloc[train_idx], self.train_df[self.features].iloc[val_idx]
y_train, y_val = self.train_df[self.target][train_idx], self.train_df[self.target][val_idx]
train_set, val_set = self.convert_dataset(x_train, y_train, x_val, y_val)
model = self.train_model(train_set, val_set)
conv_x_val = self.convert_x(x_val)
oof_pred[val_idx] = model.predict(conv_x_val).reshape(oof_pred[val_idx].shape)
x_test = self.convert_x(self.test_df[self.features])
y_pred += model.predict(x_test).reshape(y_pred.shape) / self.n_splits
print('Partial score of fold {} is: {}'.format(fold,eval_auc(oof_pred[val_idx],y_val) ))
#print(oof_pred, self.train_df[self.target].values)
loss_score = eval_auc(oof_pred,self.train_df[self.target].values)
if self.verbose:
print('Our oof AUC score is: ', loss_score)
return y_pred, loss_score, model , oof_pred
#we choose to try a LightGbM using the Base_Model class
class Lgb_Model(Base_Model):
def train_model(self, train_set, val_set):
verbosity = 100 if self.verbose else 0
return lgb.train(self.params, train_set, valid_sets=[train_set, val_set], verbose_eval=verbosity)
def convert_dataset(self, x_train, y_train, x_val, y_val):
train_set = lgb.Dataset(x_train, y_train, categorical_feature=self.categoricals)
val_set = lgb.Dataset(x_val, y_val, categorical_feature=self.categoricals)
return train_set, val_set
def get_params(self):
params = {'n_estimators':5000,
'boosting_type': 'gbdt',
'objective': 'binary',
'metric': 'auc',
'subsample': 0.75,
'subsample_freq': 1,
'learning_rate': 0.1,
'feature_fraction': 0.9,
'max_depth': 15,
'lambda_l1': 1,
'lambda_l2': 1,
'early_stopping_rounds': 100,
#'is_unbalance' : True ,
'scale_pos_weight' : 3
}
return params
def set_params(self,ps={}):
params = self.get_params()
if 'subsample_freq' in ps:
params['subsample_freq']=int(ps['subsample_freq'])
params['learning_rate']=ps['learning_rate']
params['feature_fraction']=ps['feature_fraction']
params['lambda_l1']=ps['lambda_l1']
params['lambda_l2']=ps['lambda_l2']
params['scale_pos_weight']=ps['scale_pos_weight']
params['max_depth']=int(ps['max_depth'])
return params
#we are going to drop these columns because we dont want our ML model to be bias toward these consideration
#(we also remove the target and the ids.)
to_drop = ['gender','ethnicity' ,'encounter_id', 'patient_id', 'hospital_death']
# this is a list of features that look like to be categorical
categoricals_features = ['hospital_id','ethnicity','gender','hospital_admit_source','icu_admit_source',
'icu_stay_type','icu_type','apache_3j_bodysystem','apache_2_bodysystem']
categoricals_features = [col for col in categoricals_features if col not in to_drop]
# this is the list of all input feature we would like our model to use
features = [col for col in train.columns if col not in to_drop ]
print('numerber of features ' , len(features))
print('shape of train / test ', train.shape , test.shape)
numerber of features 181 shape of train / test (91713, 186) (39308, 186)
categorical feature need to be transform to numeric for mathematical purpose. different technics of categorical encoding exists here we will rely on our model API to deal with categorical still we need to encode each categorical value to an id , for this purpose we use LabelEncoder
# categorical feature need to be transform to numeric for mathematical purpose.
# different technics of categorical encoding exists here we will rely on our model API to deal with categorical
# still we need to encode each categorical value to an id , for this purpose we use LabelEncoder
print('Transform all String features to category.\n')
for usecol in categoricals_features:
train[usecol] = train[usecol].astype('str')
test[usecol] = test[usecol].astype('str')
#Fit LabelEncoder
le = LabelEncoder().fit(
np.unique(train[usecol].unique().tolist()+
test[usecol].unique().tolist()))
#At the end 0 will be used for dropped values
train[usecol] = le.transform(train[usecol])+1
test[usecol] = le.transform(test[usecol])+1
train[usecol] = train[usecol].replace(np.nan, 0).astype('int').astype('category')
test[usecol] = test[usecol].replace(np.nan, 0).astype('int').astype('category')
Transform all String features to category.
# Drop the values above a certain threshold
# If the information contained in the variable is not that high, you can drop the variable
# if it has more than 50% missing values. In this method we are dropping columns with null values above a
# certain threshold
threshold = len(train) * 0.60
df_train_thresh = train.dropna(axis=1, thresh=threshold)
# View columns in the dataset
display(df_train_thresh.shape)
print('Columns that were removed:')
list(set(train.columns) - set(df_train_thresh.columns))
(91713, 112)
Columns that were removed:
para aqui
# percentage of death , hopefully it s a bit unbalanced
train['hospital_death'].sum()/train['hospital_death'].count()
0.08630183289173836
# You want Bayesian Optimization?
boll_BayesianOptimization = False
#boll_BayesianOptimization = True
def LGB_Beyes(subsample_freq,
learning_rate,
feature_fraction,
max_depth,
lambda_l1,
lambda_l2,
scale_pos_weight):
params={}
params['subsample_freq']=subsample_freq
params['learning_rate']=learning_rate
params['feature_fraction']=feature_fraction
params['lambda_l1']=lambda_l1
params['lambda_l2']=lambda_l2
params['max_depth']=max_depth
params['scale_pos_weight']=scale_pos_weight
lgb_model= Lgb_Model(train, test, features, categoricals=categoricals_features,ps=params)
print('auc: ',lgb_model.score)
return lgb_model.score
bounds_LGB = {
'subsample_freq': (1, 10),
'learning_rate': (0.005, 0.02),
'feature_fraction': (0.5, 1),
'lambda_l1': (0, 5),
'lambda_l2': (0, 5),
'max_depth': (13, 17),
'scale_pos_weight': (1, 10),
}
# ACTIVATE it if you want to search for better parameter
if boll_BayesianOptimization:
LGB_BO = BayesianOptimization(LGB_Beyes, bounds_LGB, random_state=1029)
import warnings
init_points = 16
n_iter = 16
with warnings.catch_warnings():
warnings.filterwarnings('ignore')
LGB_BO.maximize(init_points=init_points, n_iter=n_iter, acq='ucb', xi=0.0, alpha=1e-6)
# params = {'feature_fraction': 0.9,
# 'lambda_l1': 1,
# 'lambda_l2': 1,
# 'learning_rate': 0.1,
# 'max_depth': 13,
# 'subsample_freq': 1,
# 'scale_pos_weight':1}
# Best Hyperparams from Bayesian Optimization in notebook lgb-v2
params = {'feature_fraction': 0.524207414205945,
'lambda_l1': 4.171808735757517,
'lambda_l2': 4.6435328298317256,
'learning_rate': 0.007897539397989824,
'max_depth': 16.62053004755999,
'scale_pos_weight': 1.2199266532301127,
'subsample_freq': 1.0276518730971627}
if boll_BayesianOptimization: # ACTIVATE it if you want to search/use for better parameter
lgb_model = Lgb_Model(train,test, features, categoricals=categoricals_features, ps= LGB_BO.max['params'])
else :
lgb_model = Lgb_Model(train,test, features, categoricals=categoricals_features, ps=params)
Training until validation scores don't improve for 100 rounds [100] training's auc: 0.898677 valid_1's auc: 0.886274 [200] training's auc: 0.907174 valid_1's auc: 0.890977 [300] training's auc: 0.914479 valid_1's auc: 0.894686 [400] training's auc: 0.920499 valid_1's auc: 0.898201 [500] training's auc: 0.925519 valid_1's auc: 0.900704 [600] training's auc: 0.929659 valid_1's auc: 0.902461 [700] training's auc: 0.933339 valid_1's auc: 0.903832 [800] training's auc: 0.936776 valid_1's auc: 0.90499 [900] training's auc: 0.939956 valid_1's auc: 0.905722 [1000] training's auc: 0.942845 valid_1's auc: 0.906288 [1100] training's auc: 0.945601 valid_1's auc: 0.906736 [1200] training's auc: 0.948397 valid_1's auc: 0.907179 [1300] training's auc: 0.95104 valid_1's auc: 0.907603 [1400] training's auc: 0.953469 valid_1's auc: 0.907968 [1500] training's auc: 0.955749 valid_1's auc: 0.908317 [1600] training's auc: 0.95782 valid_1's auc: 0.908587 [1700] training's auc: 0.959813 valid_1's auc: 0.908741 [1800] training's auc: 0.961848 valid_1's auc: 0.908861 [1900] training's auc: 0.96372 valid_1's auc: 0.909003 [2000] training's auc: 0.965516 valid_1's auc: 0.909166 [2100] training's auc: 0.967129 valid_1's auc: 0.909313 [2200] training's auc: 0.968729 valid_1's auc: 0.909365 [2300] training's auc: 0.970169 valid_1's auc: 0.90946 [2400] training's auc: 0.971649 valid_1's auc: 0.909497 [2500] training's auc: 0.973017 valid_1's auc: 0.909571 [2600] training's auc: 0.974316 valid_1's auc: 0.909645 [2700] training's auc: 0.975524 valid_1's auc: 0.909721 [2800] training's auc: 0.976702 valid_1's auc: 0.909757 [2900] training's auc: 0.977867 valid_1's auc: 0.909737 Early stopping, best iteration is: [2840] training's auc: 0.977173 valid_1's auc: 0.909779 Partial score of fold 0 is: 0.9097794737342016 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.899068 valid_1's auc: 0.884299 [200] training's auc: 0.90755 valid_1's auc: 0.889147 [300] training's auc: 0.914959 valid_1's auc: 0.893216 [400] training's auc: 0.920768 valid_1's auc: 0.89633 [500] training's auc: 0.925772 valid_1's auc: 0.898884 [600] training's auc: 0.929906 valid_1's auc: 0.900712 [700] training's auc: 0.933483 valid_1's auc: 0.902192 [800] training's auc: 0.936827 valid_1's auc: 0.903358 [900] training's auc: 0.939992 valid_1's auc: 0.904171 [1000] training's auc: 0.943004 valid_1's auc: 0.904919 [1100] training's auc: 0.945924 valid_1's auc: 0.905377 [1200] training's auc: 0.948709 valid_1's auc: 0.905763 [1300] training's auc: 0.951171 valid_1's auc: 0.906128 [1400] training's auc: 0.953558 valid_1's auc: 0.906477 [1500] training's auc: 0.955828 valid_1's auc: 0.906676 [1600] training's auc: 0.958041 valid_1's auc: 0.906969 [1700] training's auc: 0.960017 valid_1's auc: 0.907271 [1800] training's auc: 0.96194 valid_1's auc: 0.907441 [1900] training's auc: 0.963908 valid_1's auc: 0.907513 [2000] training's auc: 0.965671 valid_1's auc: 0.907666 Early stopping, best iteration is: [1971] training's auc: 0.965172 valid_1's auc: 0.907705 Partial score of fold 1 is: 0.9077046995448358 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.900237 valid_1's auc: 0.882619 [200] training's auc: 0.90813 valid_1's auc: 0.886549 [300] training's auc: 0.915186 valid_1's auc: 0.890553 [400] training's auc: 0.921166 valid_1's auc: 0.894062 [500] training's auc: 0.926059 valid_1's auc: 0.896547 [600] training's auc: 0.930169 valid_1's auc: 0.898198 [700] training's auc: 0.933927 valid_1's auc: 0.899476 [800] training's auc: 0.937257 valid_1's auc: 0.90042 [900] training's auc: 0.940414 valid_1's auc: 0.901054 [1000] training's auc: 0.943383 valid_1's auc: 0.901649 [1100] training's auc: 0.946198 valid_1's auc: 0.902056 [1200] training's auc: 0.949003 valid_1's auc: 0.902441 [1300] training's auc: 0.951499 valid_1's auc: 0.902683 [1400] training's auc: 0.953793 valid_1's auc: 0.902796 [1500] training's auc: 0.956059 valid_1's auc: 0.903021 [1600] training's auc: 0.958204 valid_1's auc: 0.90335 [1700] training's auc: 0.960352 valid_1's auc: 0.903502 [1800] training's auc: 0.962306 valid_1's auc: 0.903579 [1900] training's auc: 0.964176 valid_1's auc: 0.903618 [2000] training's auc: 0.965861 valid_1's auc: 0.90371 [2100] training's auc: 0.967512 valid_1's auc: 0.903763 [2200] training's auc: 0.96908 valid_1's auc: 0.903835 [2300] training's auc: 0.970616 valid_1's auc: 0.903935 [2400] training's auc: 0.972014 valid_1's auc: 0.904008 [2500] training's auc: 0.973306 valid_1's auc: 0.90408 [2600] training's auc: 0.974593 valid_1's auc: 0.904099 [2700] training's auc: 0.975829 valid_1's auc: 0.904206 [2800] training's auc: 0.977054 valid_1's auc: 0.904207 Early stopping, best iteration is: [2733] training's auc: 0.97621 valid_1's auc: 0.904243 Partial score of fold 2 is: 0.9042428728871951 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.899447 valid_1's auc: 0.888485 [200] training's auc: 0.907282 valid_1's auc: 0.892743 [300] training's auc: 0.914599 valid_1's auc: 0.896122 [400] training's auc: 0.92077 valid_1's auc: 0.898992 [500] training's auc: 0.925777 valid_1's auc: 0.901147 [600] training's auc: 0.929985 valid_1's auc: 0.902574 [700] training's auc: 0.933669 valid_1's auc: 0.903752 [800] training's auc: 0.937096 valid_1's auc: 0.904701 [900] training's auc: 0.940262 valid_1's auc: 0.905335 [1000] training's auc: 0.943291 valid_1's auc: 0.905778 [1100] training's auc: 0.946156 valid_1's auc: 0.90625 [1200] training's auc: 0.948924 valid_1's auc: 0.906572 [1300] training's auc: 0.951548 valid_1's auc: 0.906702 [1400] training's auc: 0.95401 valid_1's auc: 0.906914 [1500] training's auc: 0.956363 valid_1's auc: 0.907025 [1600] training's auc: 0.95851 valid_1's auc: 0.907127 [1700] training's auc: 0.960485 valid_1's auc: 0.90732 [1800] training's auc: 0.962346 valid_1's auc: 0.907408 [1900] training's auc: 0.9642 valid_1's auc: 0.907486 [2000] training's auc: 0.965962 valid_1's auc: 0.907634 [2100] training's auc: 0.967628 valid_1's auc: 0.907648 [2200] training's auc: 0.969253 valid_1's auc: 0.907713 [2300] training's auc: 0.970743 valid_1's auc: 0.907753 [2400] training's auc: 0.972202 valid_1's auc: 0.907732 Early stopping, best iteration is: [2311] training's auc: 0.970898 valid_1's auc: 0.907758 Partial score of fold 3 is: 0.907757617869649 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.899023 valid_1's auc: 0.885131 [200] training's auc: 0.907248 valid_1's auc: 0.889942 [300] training's auc: 0.914309 valid_1's auc: 0.893918 [400] training's auc: 0.920175 valid_1's auc: 0.897536 [500] training's auc: 0.925375 valid_1's auc: 0.900507 [600] training's auc: 0.929591 valid_1's auc: 0.902426 [700] training's auc: 0.933307 valid_1's auc: 0.903708 [800] training's auc: 0.936729 valid_1's auc: 0.904791 [900] training's auc: 0.939917 valid_1's auc: 0.905627 [1000] training's auc: 0.942933 valid_1's auc: 0.906228 [1100] training's auc: 0.945727 valid_1's auc: 0.906642 [1200] training's auc: 0.948485 valid_1's auc: 0.907061 [1300] training's auc: 0.951069 valid_1's auc: 0.907496 [1400] training's auc: 0.953521 valid_1's auc: 0.907781 [1500] training's auc: 0.955829 valid_1's auc: 0.907985 [1600] training's auc: 0.958028 valid_1's auc: 0.90821 [1700] training's auc: 0.960065 valid_1's auc: 0.90835 [1800] training's auc: 0.962064 valid_1's auc: 0.908502 [1900] training's auc: 0.963915 valid_1's auc: 0.908636 [2000] training's auc: 0.965694 valid_1's auc: 0.908752 [2100] training's auc: 0.967341 valid_1's auc: 0.908908 [2200] training's auc: 0.968943 valid_1's auc: 0.909047 [2300] training's auc: 0.970445 valid_1's auc: 0.90909 [2400] training's auc: 0.971929 valid_1's auc: 0.909103 Early stopping, best iteration is: [2337] training's auc: 0.970987 valid_1's auc: 0.909153 Partial score of fold 4 is: 0.9091533850038696 Our oof AUC score is: 0.9076595694573828
Feature Importance from the lightgbm model (gain)
imp_df = pd.DataFrame()
imp_df['feature'] = features
imp_df['gain'] = lgb_model.model.feature_importance(importance_type='gain')
imp_df['split'] = lgb_model.model.feature_importance(importance_type='split')
def plot_importances(importances_):
mean_gain = importances_[['gain', 'feature']].groupby('feature').mean()
importances_['mean_gain'] = importances_['feature'].map(mean_gain['gain'])
plt.figure(figsize=(18, 44))
data_imp = importances_.sort_values('mean_gain', ascending=False)
sns.barplot(x='gain', y='feature', data=data_imp[:300])
plt.tight_layout()
plt.savefig('importances-lgb-v2.png')
plt.show()
plot_importances(imp_df)
import shap
explainer = shap.TreeExplainer(lgb_model.model)
shap_values = explainer.shap_values(train[features].iloc[:1000,:])
shap.summary_plot(shap_values, train[features].iloc[:1000,:])
import warnings
warnings.filterwarnings("ignore")
warnings.simplefilter(action='ignore', category=UserWarning)
i=0
for index, row in imp_df.sort_values(by=['gain'],ascending=False).iterrows():
column=row['feature']
if i< 50:
print(column,i,"gain :",row['gain'])
df1 = train.loc[train['hospital_death']==0]
df2 = train.loc[train['hospital_death']==1]
fig = plt.figure(figsize=(20,4))
sns.distplot(df1[column].dropna(), color='red', label='hospital_death 0', kde=True);
sns.distplot(df2[column].dropna(), color='blue', label='hospital_death 1', kde=True);
fig=plt.legend(loc='best')
plt.xlabel(column, fontsize=12);
plt.show()
i=i+1
apache_4a_hospital_death_prob 0 gain : 351252.365885973
hospital_id 1 gain : 150560.84422779083
apache_4a_icu_death_prob 2 gain : 146275.1173324585
d1_lactate_min 3 gain : 55353.86215758324
d1_spo2_min 4 gain : 31732.761485099792
ventilated_apache 5 gain : 28691.33399581909
d1_sysbp_min 6 gain : 24419.43428516388
d1_heartrate_min 7 gain : 21757.668104171753
age 8 gain : 21481.372059106827
d1_bun_min 9 gain : 19529.879061460495
apache_3j_diagnosis 10 gain : 18576.375860452652
gcs_motor_apache 11 gain : 16573.00981283188
d1_temp_max 12 gain : 16168.361726999283
d1_sysbp_noninvasive_min 13 gain : 16028.458354949951
d1_lactate_max 14 gain : 14038.338047266006
urineoutput_apache 15 gain : 13783.985323429108
gcs_eyes_apache 16 gain : 13053.265293121338
d1_platelets_min 17 gain : 12857.688428878784
d1_arterial_ph_min 18 gain : 12081.881103992462
d1_resprate_min 19 gain : 11519.11014342308
d1_bun_max 20 gain : 11254.286282539368
bmi 21 gain : 10985.150521993637
d1_temp_min 22 gain : 10853.892260551453
apache_3j_bodysystem 23 gain : 10575.751663684845
d1_resprate_max 24 gain : 10320.771817922592
d1_heartrate_max 25 gain : 9808.910019397736
d1_glucose_min 26 gain : 9175.222955703735
d1_wbc_min 27 gain : 9171.61188173294
d1_pao2fio2ratio_max 28 gain : 8934.969403982162
apache_2_diagnosis 29 gain : 8857.88154411316
wbc_apache 30 gain : 8504.452875375748
d1_sodium_max 31 gain : 8418.125189781189
d1_arterial_ph_max 32 gain : 7989.068685293198
d1_hco3_min 33 gain : 7691.476171255112
glucose_apache 34 gain : 7641.672451019287
pre_icu_los_days 35 gain : 7494.74694108963
h1_resprate_min 36 gain : 7485.923817396164
creatinine_apache 37 gain : 7413.3983066082
temp_apache 38 gain : 7281.159093618393
weight 39 gain : 6915.9319133758545
d1_platelets_max 40 gain : 6849.855688333511
d1_pao2fio2ratio_min 41 gain : 6763.60645365715
d1_sysbp_noninvasive_max 42 gain : 6714.262719631195
apache_2_bodysystem 43 gain : 6507.397555351257
d1_mbp_min 44 gain : 6398.961150884628
d1_hco3_max 45 gain : 6097.269358158112
d1_inr_max 46 gain : 5984.607861280441
resprate_apache 47 gain : 5856.124365568161
d1_hemaglobin_max 48 gain : 5758.485659599304
d1_mbp_noninvasive_min 49 gain : 5615.044913053513
# Find the features with zero importance
imp_df_sorted = imp_df.sort_values('gain', ascending = False)
zero_features = list(imp_df_sorted[imp_df_sorted['gain'] == 0.0]['feature'])
print('There are %d features with 0.0 importance' % len(zero_features))
imp_df_sorted.tail()
# Drop features with zero importance
print('\nLength train features: {}'.format(len(features)))
for feat_to_remove in zero_features:
print('Removing....{}'.format(feat_to_remove))
features.remove(feat_to_remove)
print('\nNew length train features: {}'.format(len(features)))
There are 4 features with 0.0 importance Length train features: 181 Removing....gcs_unable_apache Removing....lymphoma Removing....readmission_status Removing....aids New length train features: 177
# Hyper parameter tuning
boll_BayesianOptimization = True
# ACTIVATE it if you want to search for better parameter
if boll_BayesianOptimization:
LGB_BO_v2 = BayesianOptimization(LGB_Beyes, bounds_LGB, random_state=1029)
import warnings
init_points = 16
n_iter = 16
with warnings.catch_warnings():
warnings.filterwarnings('ignore')
LGB_BO_v2.maximize(init_points=init_points, n_iter=n_iter, acq='ucb', xi=0.0, alpha=1e-6)
| iter | target | featur... | lambda_l1 | lambda_l2 | learni... | max_depth | scale_... | subsam... | ------------------------------------------------------------------------------------------------------------- Training until validation scores don't improve for 100 rounds [100] training's auc: 0.915745 valid_1's auc: 0.894274 [200] training's auc: 0.929449 valid_1's auc: 0.900922 [300] training's auc: 0.939118 valid_1's auc: 0.903915 [400] training's auc: 0.947524 valid_1's auc: 0.905663 [500] training's auc: 0.954915 valid_1's auc: 0.906699 [600] training's auc: 0.96086 valid_1's auc: 0.907443 [700] training's auc: 0.966165 valid_1's auc: 0.907972 [800] training's auc: 0.970688 valid_1's auc: 0.908426 [900] training's auc: 0.974693 valid_1's auc: 0.908501 [1000] training's auc: 0.978114 valid_1's auc: 0.908663 [1100] training's auc: 0.981027 valid_1's auc: 0.908922 [1200] training's auc: 0.983655 valid_1's auc: 0.9089 [1300] training's auc: 0.985891 valid_1's auc: 0.908956 Early stopping, best iteration is: [1234] training's auc: 0.984412 valid_1's auc: 0.909113 Partial score of fold 0 is: 0.9091128593332802 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.916071 valid_1's auc: 0.892631 [200] training's auc: 0.929588 valid_1's auc: 0.899033 [300] training's auc: 0.93961 valid_1's auc: 0.90263 [400] training's auc: 0.947791 valid_1's auc: 0.904558 [500] training's auc: 0.955089 valid_1's auc: 0.905599 [600] training's auc: 0.961304 valid_1's auc: 0.906383 [700] training's auc: 0.966563 valid_1's auc: 0.907053 [800] training's auc: 0.971112 valid_1's auc: 0.907244 [900] training's auc: 0.975173 valid_1's auc: 0.90744 [1000] training's auc: 0.978587 valid_1's auc: 0.907438 [1100] training's auc: 0.981576 valid_1's auc: 0.907536 [1200] training's auc: 0.984184 valid_1's auc: 0.907559 [1300] training's auc: 0.986435 valid_1's auc: 0.907436 Early stopping, best iteration is: [1234] training's auc: 0.984925 valid_1's auc: 0.907672 Partial score of fold 1 is: 0.9076717947403574 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.916377 valid_1's auc: 0.889303 [200] training's auc: 0.930285 valid_1's auc: 0.89644 [300] training's auc: 0.93999 valid_1's auc: 0.899653 [400] training's auc: 0.948346 valid_1's auc: 0.901015 [500] training's auc: 0.955594 valid_1's auc: 0.902114 [600] training's auc: 0.961435 valid_1's auc: 0.90237 [700] training's auc: 0.966841 valid_1's auc: 0.902828 [800] training's auc: 0.971446 valid_1's auc: 0.903271 [900] training's auc: 0.975307 valid_1's auc: 0.903312 [1000] training's auc: 0.978859 valid_1's auc: 0.903358 [1100] training's auc: 0.981783 valid_1's auc: 0.903207 Early stopping, best iteration is: [1005] training's auc: 0.978992 valid_1's auc: 0.903387 Partial score of fold 2 is: 0.9033874233540437 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.916015 valid_1's auc: 0.893653 [200] training's auc: 0.929973 valid_1's auc: 0.900102 [300] training's auc: 0.939917 valid_1's auc: 0.902558 [400] training's auc: 0.948393 valid_1's auc: 0.904157 [500] training's auc: 0.955253 valid_1's auc: 0.905226 [600] training's auc: 0.961531 valid_1's auc: 0.905596 [700] training's auc: 0.966552 valid_1's auc: 0.905928 [800] training's auc: 0.971296 valid_1's auc: 0.906072 [900] training's auc: 0.975241 valid_1's auc: 0.906272 [1000] training's auc: 0.978632 valid_1's auc: 0.906601 [1100] training's auc: 0.981568 valid_1's auc: 0.90669 Early stopping, best iteration is: [1058] training's auc: 0.980272 valid_1's auc: 0.906827 Partial score of fold 3 is: 0.9068272949162964 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.916085 valid_1's auc: 0.893446 [200] training's auc: 0.930002 valid_1's auc: 0.901141 [300] training's auc: 0.940095 valid_1's auc: 0.904388 [400] training's auc: 0.948165 valid_1's auc: 0.905871 [500] training's auc: 0.955275 valid_1's auc: 0.906715 [600] training's auc: 0.961163 valid_1's auc: 0.907361 [700] training's auc: 0.966659 valid_1's auc: 0.907702 [800] training's auc: 0.971363 valid_1's auc: 0.907902 Early stopping, best iteration is: [781] training's auc: 0.970487 valid_1's auc: 0.907923 Partial score of fold 4 is: 0.9079234333014305 Our oof AUC score is: 0.9068143111709677 auc: 0.9068143111709677 | 1 | 0.9068 | 0.5205 | 2.049 | 2.944 | 0.0185 | 13.48 | 2.532 | 9.462 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.912766 valid_1's auc: 0.891295 [200] training's auc: 0.925916 valid_1's auc: 0.897542 [300] training's auc: 0.934977 valid_1's auc: 0.90108 [400] training's auc: 0.942086 valid_1's auc: 0.903123 [500] training's auc: 0.948382 valid_1's auc: 0.904424 [600] training's auc: 0.954018 valid_1's auc: 0.905343 [700] training's auc: 0.958997 valid_1's auc: 0.90604 [800] training's auc: 0.9634 valid_1's auc: 0.906576 [900] training's auc: 0.967144 valid_1's auc: 0.906882 [1000] training's auc: 0.970695 valid_1's auc: 0.907051 [1100] training's auc: 0.973587 valid_1's auc: 0.907422 [1200] training's auc: 0.976449 valid_1's auc: 0.907647 [1300] training's auc: 0.978946 valid_1's auc: 0.90785 [1400] training's auc: 0.981099 valid_1's auc: 0.907935 [1500] training's auc: 0.983129 valid_1's auc: 0.908193 [1600] training's auc: 0.984853 valid_1's auc: 0.908301 [1700] training's auc: 0.986401 valid_1's auc: 0.908385 [1800] training's auc: 0.987811 valid_1's auc: 0.908406 [1900] training's auc: 0.989151 valid_1's auc: 0.908404 Early stopping, best iteration is: [1842] training's auc: 0.988371 valid_1's auc: 0.908541 Partial score of fold 0 is: 0.9085413786396934 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.912636 valid_1's auc: 0.886772 [200] training's auc: 0.925857 valid_1's auc: 0.894136 [300] training's auc: 0.935148 valid_1's auc: 0.898635 [400] training's auc: 0.942356 valid_1's auc: 0.900809 [500] training's auc: 0.948772 valid_1's auc: 0.902057 [600] training's auc: 0.954597 valid_1's auc: 0.903011 [700] training's auc: 0.959565 valid_1's auc: 0.903947 [800] training's auc: 0.964031 valid_1's auc: 0.904266 [900] training's auc: 0.967856 valid_1's auc: 0.904531 [1000] training's auc: 0.971065 valid_1's auc: 0.90483 [1100] training's auc: 0.974138 valid_1's auc: 0.904968 [1200] training's auc: 0.976917 valid_1's auc: 0.90534 [1300] training's auc: 0.979316 valid_1's auc: 0.905216 Early stopping, best iteration is: [1218] training's auc: 0.977354 valid_1's auc: 0.905367 Partial score of fold 1 is: 0.9053673653692199 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.913166 valid_1's auc: 0.884398 [200] training's auc: 0.926498 valid_1's auc: 0.89125 [300] training's auc: 0.935552 valid_1's auc: 0.895176 [400] training's auc: 0.94298 valid_1's auc: 0.896817 [500] training's auc: 0.949304 valid_1's auc: 0.898398 [600] training's auc: 0.95491 valid_1's auc: 0.899292 [700] training's auc: 0.959992 valid_1's auc: 0.899944 [800] training's auc: 0.964452 valid_1's auc: 0.900362 [900] training's auc: 0.96801 valid_1's auc: 0.900693 [1000] training's auc: 0.97156 valid_1's auc: 0.900942 [1100] training's auc: 0.974507 valid_1's auc: 0.901038 Early stopping, best iteration is: [1066] training's auc: 0.973564 valid_1's auc: 0.901097 Partial score of fold 2 is: 0.9010969775825185 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.912766 valid_1's auc: 0.890525 [200] training's auc: 0.926168 valid_1's auc: 0.896432 [300] training's auc: 0.935238 valid_1's auc: 0.899943 [400] training's auc: 0.94271 valid_1's auc: 0.901779 [500] training's auc: 0.948933 valid_1's auc: 0.902938 [600] training's auc: 0.954891 valid_1's auc: 0.903812 [700] training's auc: 0.959745 valid_1's auc: 0.904381 [800] training's auc: 0.964125 valid_1's auc: 0.904615 [900] training's auc: 0.967925 valid_1's auc: 0.904865 [1000] training's auc: 0.971317 valid_1's auc: 0.905003 [1100] training's auc: 0.974336 valid_1's auc: 0.905219 [1200] training's auc: 0.976984 valid_1's auc: 0.905265 [1300] training's auc: 0.979433 valid_1's auc: 0.905383 Early stopping, best iteration is: [1284] training's auc: 0.979044 valid_1's auc: 0.90546 Partial score of fold 3 is: 0.905459647425656 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.91239 valid_1's auc: 0.88693 [200] training's auc: 0.925685 valid_1's auc: 0.895365 [300] training's auc: 0.934726 valid_1's auc: 0.899883 [400] training's auc: 0.942211 valid_1's auc: 0.90197 [500] training's auc: 0.948543 valid_1's auc: 0.903245 [600] training's auc: 0.953941 valid_1's auc: 0.904119 [700] training's auc: 0.959045 valid_1's auc: 0.905019 [800] training's auc: 0.96351 valid_1's auc: 0.905626 [900] training's auc: 0.967563 valid_1's auc: 0.906019 [1000] training's auc: 0.970928 valid_1's auc: 0.906479 [1100] training's auc: 0.973787 valid_1's auc: 0.906618 [1200] training's auc: 0.976549 valid_1's auc: 0.906762 [1300] training's auc: 0.979084 valid_1's auc: 0.906981 [1400] training's auc: 0.981339 valid_1's auc: 0.907093 [1500] training's auc: 0.983273 valid_1's auc: 0.907153 [1600] training's auc: 0.984906 valid_1's auc: 0.907337 Early stopping, best iteration is: [1561] training's auc: 0.984235 valid_1's auc: 0.907374 Partial score of fold 4 is: 0.9073740071287444 Our oof AUC score is: 0.9054978689616339 auc: 0.9054978689616339 | 2 | 0.9055 | 0.7977 | 1.678 | 2.208 | 0.01244 | 13.35 | 3.467 | 6.94 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.908657 valid_1's auc: 0.888882 [200] training's auc: 0.917431 valid_1's auc: 0.893119 [300] training's auc: 0.924325 valid_1's auc: 0.896354 [400] training's auc: 0.930229 valid_1's auc: 0.898766 [500] training's auc: 0.935376 valid_1's auc: 0.900695 [600] training's auc: 0.939868 valid_1's auc: 0.902106 [700] training's auc: 0.943861 valid_1's auc: 0.903082 [800] training's auc: 0.947472 valid_1's auc: 0.904062 [900] training's auc: 0.950905 valid_1's auc: 0.904573 [1000] training's auc: 0.954048 valid_1's auc: 0.904843 [1100] training's auc: 0.957003 valid_1's auc: 0.905257 [1200] training's auc: 0.959776 valid_1's auc: 0.905614 [1300] training's auc: 0.962316 valid_1's auc: 0.906016 [1400] training's auc: 0.964673 valid_1's auc: 0.906287 [1500] training's auc: 0.966939 valid_1's auc: 0.906504 [1600] training's auc: 0.969012 valid_1's auc: 0.906666 [1700] training's auc: 0.970843 valid_1's auc: 0.906698 Early stopping, best iteration is: [1643] training's auc: 0.969813 valid_1's auc: 0.90677 Partial score of fold 0 is: 0.9067698714111903 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.909158 valid_1's auc: 0.886808 [200] training's auc: 0.917844 valid_1's auc: 0.891624 [300] training's auc: 0.924763 valid_1's auc: 0.895132 [400] training's auc: 0.930819 valid_1's auc: 0.898123 [500] training's auc: 0.935978 valid_1's auc: 0.900254 [600] training's auc: 0.940409 valid_1's auc: 0.901693 [700] training's auc: 0.944429 valid_1's auc: 0.902818 [800] training's auc: 0.94802 valid_1's auc: 0.903661 [900] training's auc: 0.95149 valid_1's auc: 0.904273 [1000] training's auc: 0.954591 valid_1's auc: 0.904755 [1100] training's auc: 0.957557 valid_1's auc: 0.905084 [1200] training's auc: 0.960332 valid_1's auc: 0.90537 [1300] training's auc: 0.962835 valid_1's auc: 0.905678 [1400] training's auc: 0.965158 valid_1's auc: 0.905914 [1500] training's auc: 0.967432 valid_1's auc: 0.906025 [1600] training's auc: 0.969472 valid_1's auc: 0.906148 [1700] training's auc: 0.971394 valid_1's auc: 0.906264 [1800] training's auc: 0.973107 valid_1's auc: 0.90629 [1900] training's auc: 0.974779 valid_1's auc: 0.906392 [2000] training's auc: 0.976335 valid_1's auc: 0.906434 [2100] training's auc: 0.977811 valid_1's auc: 0.90648 [2200] training's auc: 0.979225 valid_1's auc: 0.906514 [2300] training's auc: 0.980492 valid_1's auc: 0.906463 Early stopping, best iteration is: [2211] training's auc: 0.979362 valid_1's auc: 0.906538 Partial score of fold 1 is: 0.906537954730829 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.909537 valid_1's auc: 0.88415 [200] training's auc: 0.91813 valid_1's auc: 0.887808 [300] training's auc: 0.925029 valid_1's auc: 0.891544 [400] training's auc: 0.93117 valid_1's auc: 0.894647 [500] training's auc: 0.93617 valid_1's auc: 0.89687 [600] training's auc: 0.940731 valid_1's auc: 0.898245 [700] training's auc: 0.944703 valid_1's auc: 0.899361 [800] training's auc: 0.948314 valid_1's auc: 0.900177 [900] training's auc: 0.951672 valid_1's auc: 0.900846 [1000] training's auc: 0.954769 valid_1's auc: 0.901425 [1100] training's auc: 0.957751 valid_1's auc: 0.901735 [1200] training's auc: 0.960513 valid_1's auc: 0.902019 [1300] training's auc: 0.963006 valid_1's auc: 0.902309 [1400] training's auc: 0.965325 valid_1's auc: 0.902574 [1500] training's auc: 0.967445 valid_1's auc: 0.902626 [1600] training's auc: 0.969591 valid_1's auc: 0.902791 [1700] training's auc: 0.971465 valid_1's auc: 0.902807 Early stopping, best iteration is: [1672] training's auc: 0.970959 valid_1's auc: 0.902838 Partial score of fold 2 is: 0.9028375399719878 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.909349 valid_1's auc: 0.888174 [200] training's auc: 0.917738 valid_1's auc: 0.891826 [300] training's auc: 0.924703 valid_1's auc: 0.894905 [400] training's auc: 0.930723 valid_1's auc: 0.897458 [500] training's auc: 0.935848 valid_1's auc: 0.899443 [600] training's auc: 0.940387 valid_1's auc: 0.901036 [700] training's auc: 0.944397 valid_1's auc: 0.902095 [800] training's auc: 0.948068 valid_1's auc: 0.902939 [900] training's auc: 0.95163 valid_1's auc: 0.903561 [1000] training's auc: 0.954812 valid_1's auc: 0.904064 [1100] training's auc: 0.957777 valid_1's auc: 0.904471 [1200] training's auc: 0.96058 valid_1's auc: 0.904788 [1300] training's auc: 0.963081 valid_1's auc: 0.90507 [1400] training's auc: 0.96541 valid_1's auc: 0.905315 [1500] training's auc: 0.967671 valid_1's auc: 0.905373 [1600] training's auc: 0.969727 valid_1's auc: 0.905465 [1700] training's auc: 0.971578 valid_1's auc: 0.905607 [1800] training's auc: 0.97333 valid_1's auc: 0.90566 Early stopping, best iteration is: [1791] training's auc: 0.973199 valid_1's auc: 0.905669 Partial score of fold 3 is: 0.9056689239151424 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.90925 valid_1's auc: 0.884657 [200] training's auc: 0.917603 valid_1's auc: 0.889814 [300] training's auc: 0.924462 valid_1's auc: 0.89455 [400] training's auc: 0.930475 valid_1's auc: 0.898051 [500] training's auc: 0.935592 valid_1's auc: 0.900703 [600] training's auc: 0.940012 valid_1's auc: 0.902313 [700] training's auc: 0.944098 valid_1's auc: 0.90344 [800] training's auc: 0.947787 valid_1's auc: 0.90445 [900] training's auc: 0.951201 valid_1's auc: 0.905061 [1000] training's auc: 0.954377 valid_1's auc: 0.905632 [1100] training's auc: 0.957365 valid_1's auc: 0.906 [1200] training's auc: 0.960188 valid_1's auc: 0.906329 [1300] training's auc: 0.962697 valid_1's auc: 0.906475 [1400] training's auc: 0.965005 valid_1's auc: 0.906638 [1500] training's auc: 0.967156 valid_1's auc: 0.906691 [1600] training's auc: 0.969191 valid_1's auc: 0.906778 [1700] training's auc: 0.971138 valid_1's auc: 0.906864 [1800] training's auc: 0.972932 valid_1's auc: 0.906889 Early stopping, best iteration is: [1712] training's auc: 0.971374 valid_1's auc: 0.906905 Partial score of fold 4 is: 0.9069048312525488 Our oof AUC score is: 0.9056476455571794 auc: 0.9056476455571794 | 3 | 0.9056 | 0.5402 | 0.1933 | 3.376 | 0.00781 | 16.76 | 8.841 | 4.225 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.918145 valid_1's auc: 0.889415 [200] training's auc: 0.932454 valid_1's auc: 0.896559 [300] training's auc: 0.94212 valid_1's auc: 0.900122 [400] training's auc: 0.94986 valid_1's auc: 0.902318 [500] training's auc: 0.956364 valid_1's auc: 0.903426 [600] training's auc: 0.961828 valid_1's auc: 0.904211 [700] training's auc: 0.966678 valid_1's auc: 0.904611 [800] training's auc: 0.970928 valid_1's auc: 0.9048 [900] training's auc: 0.974415 valid_1's auc: 0.905195 [1000] training's auc: 0.977447 valid_1's auc: 0.905665 [1100] training's auc: 0.980154 valid_1's auc: 0.905634 Early stopping, best iteration is: [1065] training's auc: 0.979239 valid_1's auc: 0.905733 Partial score of fold 0 is: 0.9057328989245821 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.917697 valid_1's auc: 0.885852 [200] training's auc: 0.932627 valid_1's auc: 0.894352 [300] training's auc: 0.942454 valid_1's auc: 0.898454 [400] training's auc: 0.950239 valid_1's auc: 0.900105 [500] training's auc: 0.956845 valid_1's auc: 0.901149 [600] training's auc: 0.962488 valid_1's auc: 0.902108 [700] training's auc: 0.967313 valid_1's auc: 0.902437 [800] training's auc: 0.971372 valid_1's auc: 0.902628 [900] training's auc: 0.974875 valid_1's auc: 0.902709 [1000] training's auc: 0.977881 valid_1's auc: 0.902938 [1100] training's auc: 0.980573 valid_1's auc: 0.90306 [1200] training's auc: 0.982916 valid_1's auc: 0.903398 [1300] training's auc: 0.984958 valid_1's auc: 0.90338 Early stopping, best iteration is: [1201] training's auc: 0.982937 valid_1's auc: 0.903409 Partial score of fold 1 is: 0.9034094729652921 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.918536 valid_1's auc: 0.884052 [200] training's auc: 0.933217 valid_1's auc: 0.892225 [300] training's auc: 0.942872 valid_1's auc: 0.896093 [400] training's auc: 0.950757 valid_1's auc: 0.897979 [500] training's auc: 0.957211 valid_1's auc: 0.899402 [600] training's auc: 0.962767 valid_1's auc: 0.899876 [700] training's auc: 0.967398 valid_1's auc: 0.900417 [800] training's auc: 0.971538 valid_1's auc: 0.90117 [900] training's auc: 0.974865 valid_1's auc: 0.901497 Early stopping, best iteration is: [897] training's auc: 0.97478 valid_1's auc: 0.90154 Partial score of fold 2 is: 0.9015398920812872 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.917932 valid_1's auc: 0.889281 [200] training's auc: 0.932157 valid_1's auc: 0.895821 [300] training's auc: 0.94216 valid_1's auc: 0.899024 [400] training's auc: 0.950111 valid_1's auc: 0.900637 [500] training's auc: 0.956766 valid_1's auc: 0.901835 [600] training's auc: 0.962436 valid_1's auc: 0.902367 [700] training's auc: 0.967192 valid_1's auc: 0.902877 [800] training's auc: 0.971374 valid_1's auc: 0.903135 [900] training's auc: 0.974826 valid_1's auc: 0.903373 [1000] training's auc: 0.97792 valid_1's auc: 0.903621 [1100] training's auc: 0.980693 valid_1's auc: 0.903729 Early stopping, best iteration is: [1093] training's auc: 0.980512 valid_1's auc: 0.903781 Partial score of fold 3 is: 0.9037813268755153 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.917653 valid_1's auc: 0.885118 [200] training's auc: 0.932133 valid_1's auc: 0.895166 [300] training's auc: 0.942038 valid_1's auc: 0.899439 [400] training's auc: 0.949898 valid_1's auc: 0.901547 [500] training's auc: 0.956485 valid_1's auc: 0.902955 [600] training's auc: 0.962018 valid_1's auc: 0.903831 [700] training's auc: 0.966907 valid_1's auc: 0.904567 [800] training's auc: 0.97098 valid_1's auc: 0.904879 [900] training's auc: 0.974536 valid_1's auc: 0.905163 [1000] training's auc: 0.977648 valid_1's auc: 0.905392 Early stopping, best iteration is: [988] training's auc: 0.977335 valid_1's auc: 0.905447 Partial score of fold 4 is: 0.9054473215229071 Our oof AUC score is: 0.9038778540284516 auc: 0.9038778540284516 | 4 | 0.9039 | 0.7839 | 4.396 | 2.262 | 0.01558 | 16.18 | 9.494 | 3.734 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.909152 valid_1's auc: 0.887679 [200] training's auc: 0.921142 valid_1's auc: 0.892401 [300] training's auc: 0.929959 valid_1's auc: 0.896668 [400] training's auc: 0.936745 valid_1's auc: 0.899254 [500] training's auc: 0.942353 valid_1's auc: 0.900777 [600] training's auc: 0.947276 valid_1's auc: 0.901873 [700] training's auc: 0.951922 valid_1's auc: 0.902769 [800] training's auc: 0.956011 valid_1's auc: 0.903324 [900] training's auc: 0.959557 valid_1's auc: 0.903894 [1000] training's auc: 0.962964 valid_1's auc: 0.904548 [1100] training's auc: 0.966202 valid_1's auc: 0.904924 [1200] training's auc: 0.968983 valid_1's auc: 0.905235 [1300] training's auc: 0.971427 valid_1's auc: 0.905294 [1400] training's auc: 0.973673 valid_1's auc: 0.905479 [1500] training's auc: 0.975885 valid_1's auc: 0.905478 [1600] training's auc: 0.97777 valid_1's auc: 0.905583 Early stopping, best iteration is: [1586] training's auc: 0.977501 valid_1's auc: 0.905628 Partial score of fold 0 is: 0.9056281915398845 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.909779 valid_1's auc: 0.883923 [200] training's auc: 0.921824 valid_1's auc: 0.889637 [300] training's auc: 0.930907 valid_1's auc: 0.894182 [400] training's auc: 0.937673 valid_1's auc: 0.896862 [500] training's auc: 0.943216 valid_1's auc: 0.898752 [600] training's auc: 0.948135 valid_1's auc: 0.899915 [700] training's auc: 0.952528 valid_1's auc: 0.900943 [800] training's auc: 0.956627 valid_1's auc: 0.901305 [900] training's auc: 0.960295 valid_1's auc: 0.902044 [1000] training's auc: 0.963669 valid_1's auc: 0.902524 [1100] training's auc: 0.9668 valid_1's auc: 0.902757 [1200] training's auc: 0.969514 valid_1's auc: 0.903067 [1300] training's auc: 0.97207 valid_1's auc: 0.903284 [1400] training's auc: 0.974311 valid_1's auc: 0.90329 [1500] training's auc: 0.976499 valid_1's auc: 0.903496 [1600] training's auc: 0.978454 valid_1's auc: 0.903487 Early stopping, best iteration is: [1510] training's auc: 0.976683 valid_1's auc: 0.903586 Partial score of fold 1 is: 0.9035864352299265 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.909962 valid_1's auc: 0.881995 [200] training's auc: 0.921838 valid_1's auc: 0.887192 [300] training's auc: 0.930657 valid_1's auc: 0.891603 [400] training's auc: 0.937406 valid_1's auc: 0.894124 [500] training's auc: 0.943137 valid_1's auc: 0.89563 [600] training's auc: 0.9482 valid_1's auc: 0.896814 [700] training's auc: 0.952652 valid_1's auc: 0.89763 [800] training's auc: 0.956678 valid_1's auc: 0.898426 [900] training's auc: 0.960259 valid_1's auc: 0.899019 [1000] training's auc: 0.963715 valid_1's auc: 0.899417 [1100] training's auc: 0.966674 valid_1's auc: 0.899607 [1200] training's auc: 0.969499 valid_1's auc: 0.899756 [1300] training's auc: 0.972049 valid_1's auc: 0.900014 [1400] training's auc: 0.974318 valid_1's auc: 0.900295 [1500] training's auc: 0.976373 valid_1's auc: 0.900449 [1600] training's auc: 0.97831 valid_1's auc: 0.900564 [1700] training's auc: 0.98012 valid_1's auc: 0.90071 [1800] training's auc: 0.981745 valid_1's auc: 0.900683 [1900] training's auc: 0.983309 valid_1's auc: 0.900733 Early stopping, best iteration is: [1852] training's auc: 0.982569 valid_1's auc: 0.900788 Partial score of fold 2 is: 0.9007877553420367 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.909365 valid_1's auc: 0.886831 [200] training's auc: 0.921161 valid_1's auc: 0.891653 [300] training's auc: 0.930461 valid_1's auc: 0.895446 [400] training's auc: 0.937486 valid_1's auc: 0.897959 [500] training's auc: 0.943189 valid_1's auc: 0.899616 [600] training's auc: 0.948175 valid_1's auc: 0.900653 [700] training's auc: 0.952779 valid_1's auc: 0.901336 [800] training's auc: 0.956983 valid_1's auc: 0.901946 [900] training's auc: 0.960639 valid_1's auc: 0.902348 [1000] training's auc: 0.964092 valid_1's auc: 0.902604 [1100] training's auc: 0.967086 valid_1's auc: 0.902792 [1200] training's auc: 0.969747 valid_1's auc: 0.903037 [1300] training's auc: 0.972337 valid_1's auc: 0.903036 [1400] training's auc: 0.97465 valid_1's auc: 0.903194 [1500] training's auc: 0.976749 valid_1's auc: 0.903438 [1600] training's auc: 0.978584 valid_1's auc: 0.903481 [1700] training's auc: 0.980353 valid_1's auc: 0.903695 [1800] training's auc: 0.981916 valid_1's auc: 0.90354 Early stopping, best iteration is: [1702] training's auc: 0.980391 valid_1's auc: 0.903698 Partial score of fold 3 is: 0.9036978725981876 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.909524 valid_1's auc: 0.884025 [200] training's auc: 0.921531 valid_1's auc: 0.890292 [300] training's auc: 0.930467 valid_1's auc: 0.895962 [400] training's auc: 0.937065 valid_1's auc: 0.899065 [500] training's auc: 0.942788 valid_1's auc: 0.901071 [600] training's auc: 0.94781 valid_1's auc: 0.901922 [700] training's auc: 0.952249 valid_1's auc: 0.902944 [800] training's auc: 0.956235 valid_1's auc: 0.903625 [900] training's auc: 0.960056 valid_1's auc: 0.903962 [1000] training's auc: 0.963273 valid_1's auc: 0.904411 [1100] training's auc: 0.966322 valid_1's auc: 0.904777 [1200] training's auc: 0.969143 valid_1's auc: 0.90496 [1300] training's auc: 0.971763 valid_1's auc: 0.905119 [1400] training's auc: 0.974055 valid_1's auc: 0.905042 Early stopping, best iteration is: [1345] training's auc: 0.972839 valid_1's auc: 0.905201 Partial score of fold 4 is: 0.9052005396106831 Our oof AUC score is: 0.9036425093300728 auc: 0.9036425093300728 | 5 | 0.9036 | 0.9548 | 2.5 | 0.194 | 0.009008 | 13.47 | 3.556 | 5.414 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.919486 valid_1's auc: 0.892912 [200] training's auc: 0.934753 valid_1's auc: 0.899217 [300] training's auc: 0.945139 valid_1's auc: 0.902067 [400] training's auc: 0.953371 valid_1's auc: 0.903331 [500] training's auc: 0.960479 valid_1's auc: 0.904163 [600] training's auc: 0.966493 valid_1's auc: 0.904857 [700] training's auc: 0.971523 valid_1's auc: 0.905104 [800] training's auc: 0.975569 valid_1's auc: 0.905245 [900] training's auc: 0.978993 valid_1's auc: 0.905294 Early stopping, best iteration is: [872] training's auc: 0.978106 valid_1's auc: 0.905473 Partial score of fold 0 is: 0.9054730150449963 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.920447 valid_1's auc: 0.890195 [200] training's auc: 0.935763 valid_1's auc: 0.897378 [300] training's auc: 0.945971 valid_1's auc: 0.900168 [400] training's auc: 0.954325 valid_1's auc: 0.90192 [500] training's auc: 0.961302 valid_1's auc: 0.903005 [600] training's auc: 0.967283 valid_1's auc: 0.903265 [700] training's auc: 0.97204 valid_1's auc: 0.903676 [800] training's auc: 0.976101 valid_1's auc: 0.903899 [900] training's auc: 0.979603 valid_1's auc: 0.904015 [1000] training's auc: 0.982442 valid_1's auc: 0.90426 [1100] training's auc: 0.984965 valid_1's auc: 0.903842 Early stopping, best iteration is: [1005] training's auc: 0.982563 valid_1's auc: 0.904269 Partial score of fold 1 is: 0.9042687293544025 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.920238 valid_1's auc: 0.887972 [200] training's auc: 0.935694 valid_1's auc: 0.895648 [300] training's auc: 0.945955 valid_1's auc: 0.898447 [400] training's auc: 0.954473 valid_1's auc: 0.899597 [500] training's auc: 0.961537 valid_1's auc: 0.900566 [600] training's auc: 0.967331 valid_1's auc: 0.900968 [700] training's auc: 0.972197 valid_1's auc: 0.901256 [800] training's auc: 0.976349 valid_1's auc: 0.901546 Early stopping, best iteration is: [760] training's auc: 0.974676 valid_1's auc: 0.901606 Partial score of fold 2 is: 0.9016060409150325 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.919912 valid_1's auc: 0.892302 [200] training's auc: 0.935063 valid_1's auc: 0.898482 [300] training's auc: 0.945573 valid_1's auc: 0.901206 [400] training's auc: 0.954142 valid_1's auc: 0.902549 [500] training's auc: 0.960983 valid_1's auc: 0.903288 [600] training's auc: 0.967023 valid_1's auc: 0.903666 [700] training's auc: 0.971755 valid_1's auc: 0.903885 [800] training's auc: 0.975864 valid_1's auc: 0.904136 [900] training's auc: 0.979489 valid_1's auc: 0.904325 Early stopping, best iteration is: [852] training's auc: 0.977743 valid_1's auc: 0.904368 Partial score of fold 3 is: 0.9043681453892625 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.919552 valid_1's auc: 0.890545 [200] training's auc: 0.934851 valid_1's auc: 0.898636 [300] training's auc: 0.945287 valid_1's auc: 0.901562 [400] training's auc: 0.953685 valid_1's auc: 0.903057 [500] training's auc: 0.960873 valid_1's auc: 0.903826 [600] training's auc: 0.966705 valid_1's auc: 0.904337 [700] training's auc: 0.97173 valid_1's auc: 0.904602 [800] training's auc: 0.975777 valid_1's auc: 0.904772 [900] training's auc: 0.979268 valid_1's auc: 0.904923 [1000] training's auc: 0.982226 valid_1's auc: 0.904916 Early stopping, best iteration is: [923] training's auc: 0.979997 valid_1's auc: 0.905012 Partial score of fold 4 is: 0.9050117309046606 Our oof AUC score is: 0.9041076624461521 auc: 0.9041076624461521 | 6 | 0.9041 | 0.8786 | 3.534 | 2.64 | 0.01805 | 13.09 | 3.774 | 2.515 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.910682 valid_1's auc: 0.88921 [200] training's auc: 0.920821 valid_1's auc: 0.894234 [300] training's auc: 0.929203 valid_1's auc: 0.898141 [400] training's auc: 0.935568 valid_1's auc: 0.900393 [500] training's auc: 0.941019 valid_1's auc: 0.902047 [600] training's auc: 0.945654 valid_1's auc: 0.903438 [700] training's auc: 0.950068 valid_1's auc: 0.904237 [800] training's auc: 0.954031 valid_1's auc: 0.905053 [900] training's auc: 0.957758 valid_1's auc: 0.90558 [1000] training's auc: 0.961044 valid_1's auc: 0.906048 [1100] training's auc: 0.964058 valid_1's auc: 0.906322 [1200] training's auc: 0.96684 valid_1's auc: 0.906611 [1300] training's auc: 0.969325 valid_1's auc: 0.906864 [1400] training's auc: 0.971609 valid_1's auc: 0.907012 [1500] training's auc: 0.97377 valid_1's auc: 0.907191 [1600] training's auc: 0.975716 valid_1's auc: 0.907388 [1700] training's auc: 0.977448 valid_1's auc: 0.90749 Early stopping, best iteration is: [1672] training's auc: 0.976975 valid_1's auc: 0.907554 Partial score of fold 0 is: 0.9075537068223382 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.910998 valid_1's auc: 0.885598 [200] training's auc: 0.921194 valid_1's auc: 0.891451 [300] training's auc: 0.929507 valid_1's auc: 0.89585 [400] training's auc: 0.935973 valid_1's auc: 0.898331 [500] training's auc: 0.941429 valid_1's auc: 0.900271 [600] training's auc: 0.946233 valid_1's auc: 0.901588 [700] training's auc: 0.950568 valid_1's auc: 0.902526 [800] training's auc: 0.954484 valid_1's auc: 0.903165 [900] training's auc: 0.95811 valid_1's auc: 0.903651 [1000] training's auc: 0.961459 valid_1's auc: 0.904185 [1100] training's auc: 0.9645 valid_1's auc: 0.90457 [1200] training's auc: 0.967318 valid_1's auc: 0.904817 [1300] training's auc: 0.969788 valid_1's auc: 0.90494 [1400] training's auc: 0.972069 valid_1's auc: 0.904892 Early stopping, best iteration is: [1333] training's auc: 0.970567 valid_1's auc: 0.905003 Partial score of fold 1 is: 0.9050034148628702 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.911044 valid_1's auc: 0.883107 [200] training's auc: 0.921754 valid_1's auc: 0.888583 [300] training's auc: 0.929914 valid_1's auc: 0.893007 [400] training's auc: 0.936475 valid_1's auc: 0.89575 [500] training's auc: 0.941889 valid_1's auc: 0.897648 [600] training's auc: 0.946607 valid_1's auc: 0.898832 [700] training's auc: 0.950977 valid_1's auc: 0.899565 [800] training's auc: 0.954944 valid_1's auc: 0.900132 [900] training's auc: 0.958458 valid_1's auc: 0.900615 [1000] training's auc: 0.961804 valid_1's auc: 0.90101 [1100] training's auc: 0.964868 valid_1's auc: 0.901293 [1200] training's auc: 0.967562 valid_1's auc: 0.901608 [1300] training's auc: 0.970071 valid_1's auc: 0.901754 [1400] training's auc: 0.972297 valid_1's auc: 0.901968 [1500] training's auc: 0.974417 valid_1's auc: 0.902174 [1600] training's auc: 0.976354 valid_1's auc: 0.902315 [1700] training's auc: 0.978118 valid_1's auc: 0.90235 Early stopping, best iteration is: [1685] training's auc: 0.977871 valid_1's auc: 0.902373 Partial score of fold 2 is: 0.9023727266285428 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.910938 valid_1's auc: 0.887987 [200] training's auc: 0.921307 valid_1's auc: 0.892677 [300] training's auc: 0.929551 valid_1's auc: 0.896324 [400] training's auc: 0.936153 valid_1's auc: 0.898571 [500] training's auc: 0.941589 valid_1's auc: 0.900288 [600] training's auc: 0.946379 valid_1's auc: 0.901567 [700] training's auc: 0.950608 valid_1's auc: 0.902303 [800] training's auc: 0.95472 valid_1's auc: 0.902869 [900] training's auc: 0.958453 valid_1's auc: 0.903298 [1000] training's auc: 0.961768 valid_1's auc: 0.903727 [1100] training's auc: 0.964754 valid_1's auc: 0.904069 [1200] training's auc: 0.9675 valid_1's auc: 0.904257 [1300] training's auc: 0.969997 valid_1's auc: 0.904408 Early stopping, best iteration is: [1285] training's auc: 0.969623 valid_1's auc: 0.904458 Partial score of fold 3 is: 0.9044578945465871 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.910457 valid_1's auc: 0.883333 [200] training's auc: 0.920638 valid_1's auc: 0.890055 [300] training's auc: 0.928844 valid_1's auc: 0.895639 [400] training's auc: 0.935515 valid_1's auc: 0.898907 [500] training's auc: 0.941048 valid_1's auc: 0.901051 [600] training's auc: 0.945851 valid_1's auc: 0.902404 [700] training's auc: 0.95033 valid_1's auc: 0.903388 [800] training's auc: 0.954346 valid_1's auc: 0.904072 [900] training's auc: 0.958017 valid_1's auc: 0.904671 [1000] training's auc: 0.961284 valid_1's auc: 0.905104 [1100] training's auc: 0.964279 valid_1's auc: 0.905259 [1200] training's auc: 0.967093 valid_1's auc: 0.905546 [1300] training's auc: 0.969694 valid_1's auc: 0.905673 [1400] training's auc: 0.972005 valid_1's auc: 0.905801 [1500] training's auc: 0.974048 valid_1's auc: 0.905939 [1600] training's auc: 0.976013 valid_1's auc: 0.905958 Early stopping, best iteration is: [1511] training's auc: 0.97426 valid_1's auc: 0.906008 Partial score of fold 4 is: 0.9060080181693607 Our oof AUC score is: 0.9049929969517136 auc: 0.9049929969517136 | 7 | 0.905 | 0.713 | 3.884 | 2.672 | 0.009373 | 14.89 | 7.059 | 3.298 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.908024 valid_1's auc: 0.884948 [200] training's auc: 0.917803 valid_1's auc: 0.889454 [300] training's auc: 0.925549 valid_1's auc: 0.893573 [400] training's auc: 0.932076 valid_1's auc: 0.896423 [500] training's auc: 0.937366 valid_1's auc: 0.898437 [600] training's auc: 0.94197 valid_1's auc: 0.89992 [700] training's auc: 0.946047 valid_1's auc: 0.901016 [800] training's auc: 0.94962 valid_1's auc: 0.901851 [900] training's auc: 0.952951 valid_1's auc: 0.902492 [1000] training's auc: 0.956025 valid_1's auc: 0.903054 [1100] training's auc: 0.958917 valid_1's auc: 0.903489 [1200] training's auc: 0.961606 valid_1's auc: 0.903903 [1300] training's auc: 0.964065 valid_1's auc: 0.904132 [1400] training's auc: 0.966325 valid_1's auc: 0.904449 [1500] training's auc: 0.968458 valid_1's auc: 0.904681 [1600] training's auc: 0.970377 valid_1's auc: 0.904878 [1700] training's auc: 0.972138 valid_1's auc: 0.905081 [1800] training's auc: 0.973859 valid_1's auc: 0.905242 [1900] training's auc: 0.975505 valid_1's auc: 0.905378 [2000] training's auc: 0.976989 valid_1's auc: 0.905541 [2100] training's auc: 0.97836 valid_1's auc: 0.90555 [2200] training's auc: 0.979714 valid_1's auc: 0.905571 Early stopping, best iteration is: [2136] training's auc: 0.97884 valid_1's auc: 0.905599 Partial score of fold 0 is: 0.905598528216718 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.908467 valid_1's auc: 0.880925 [200] training's auc: 0.9182 valid_1's auc: 0.886223 [300] training's auc: 0.926152 valid_1's auc: 0.890916 [400] training's auc: 0.932738 valid_1's auc: 0.894476 [500] training's auc: 0.937983 valid_1's auc: 0.896713 [600] training's auc: 0.94248 valid_1's auc: 0.898299 [700] training's auc: 0.946476 valid_1's auc: 0.8995 [800] training's auc: 0.950083 valid_1's auc: 0.900445 [900] training's auc: 0.953429 valid_1's auc: 0.901089 [1000] training's auc: 0.956553 valid_1's auc: 0.901533 [1100] training's auc: 0.959449 valid_1's auc: 0.9019 [1200] training's auc: 0.962161 valid_1's auc: 0.902271 [1300] training's auc: 0.964619 valid_1's auc: 0.902474 [1400] training's auc: 0.966838 valid_1's auc: 0.902724 [1500] training's auc: 0.968987 valid_1's auc: 0.902945 [1600] training's auc: 0.970938 valid_1's auc: 0.903065 [1700] training's auc: 0.972754 valid_1's auc: 0.903242 [1800] training's auc: 0.974451 valid_1's auc: 0.903398 [1900] training's auc: 0.975999 valid_1's auc: 0.903504 [2000] training's auc: 0.977479 valid_1's auc: 0.90362 [2100] training's auc: 0.978879 valid_1's auc: 0.903714 [2200] training's auc: 0.98016 valid_1's auc: 0.90373 [2300] training's auc: 0.981402 valid_1's auc: 0.903801 Early stopping, best iteration is: [2299] training's auc: 0.981389 valid_1's auc: 0.903809 Partial score of fold 1 is: 0.9038087782329254 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.908474 valid_1's auc: 0.878654 [200] training's auc: 0.918439 valid_1's auc: 0.88337 [300] training's auc: 0.926378 valid_1's auc: 0.888184 [400] training's auc: 0.932946 valid_1's auc: 0.891508 [500] training's auc: 0.938184 valid_1's auc: 0.893892 [600] training's auc: 0.942765 valid_1's auc: 0.895513 [700] training's auc: 0.94681 valid_1's auc: 0.896721 [800] training's auc: 0.950552 valid_1's auc: 0.89778 [900] training's auc: 0.953868 valid_1's auc: 0.898323 [1000] training's auc: 0.956936 valid_1's auc: 0.898812 [1100] training's auc: 0.959813 valid_1's auc: 0.899225 [1200] training's auc: 0.962432 valid_1's auc: 0.899649 [1300] training's auc: 0.964838 valid_1's auc: 0.900015 [1400] training's auc: 0.967097 valid_1's auc: 0.900225 [1500] training's auc: 0.969185 valid_1's auc: 0.900424 [1600] training's auc: 0.971172 valid_1's auc: 0.900672 [1700] training's auc: 0.972985 valid_1's auc: 0.900851 [1800] training's auc: 0.974649 valid_1's auc: 0.900836 Early stopping, best iteration is: [1713] training's auc: 0.973215 valid_1's auc: 0.900879 Partial score of fold 2 is: 0.900879006810126 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.908177 valid_1's auc: 0.884712 [200] training's auc: 0.917946 valid_1's auc: 0.888625 [300] training's auc: 0.925642 valid_1's auc: 0.891972 [400] training's auc: 0.932197 valid_1's auc: 0.894999 [500] training's auc: 0.9376 valid_1's auc: 0.897319 [600] training's auc: 0.942259 valid_1's auc: 0.898725 [700] training's auc: 0.946407 valid_1's auc: 0.899876 [800] training's auc: 0.950054 valid_1's auc: 0.9008 [900] training's auc: 0.953452 valid_1's auc: 0.901462 [1000] training's auc: 0.95654 valid_1's auc: 0.901984 [1100] training's auc: 0.959456 valid_1's auc: 0.902293 [1200] training's auc: 0.962131 valid_1's auc: 0.902598 [1300] training's auc: 0.964608 valid_1's auc: 0.902821 [1400] training's auc: 0.966938 valid_1's auc: 0.903146 [1500] training's auc: 0.969064 valid_1's auc: 0.903144 [1600] training's auc: 0.971026 valid_1's auc: 0.903145 Early stopping, best iteration is: [1513] training's auc: 0.969331 valid_1's auc: 0.90319 Partial score of fold 3 is: 0.9031904751153027 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.908431 valid_1's auc: 0.879332 [200] training's auc: 0.918008 valid_1's auc: 0.884886 [300] training's auc: 0.925734 valid_1's auc: 0.889958 [400] training's auc: 0.932165 valid_1's auc: 0.894183 [500] training's auc: 0.937444 valid_1's auc: 0.897241 [600] training's auc: 0.941923 valid_1's auc: 0.899079 [700] training's auc: 0.946026 valid_1's auc: 0.900491 [800] training's auc: 0.949697 valid_1's auc: 0.901604 [900] training's auc: 0.953109 valid_1's auc: 0.902347 [1000] training's auc: 0.95619 valid_1's auc: 0.902957 [1100] training's auc: 0.959107 valid_1's auc: 0.903363 [1200] training's auc: 0.961784 valid_1's auc: 0.90381 [1300] training's auc: 0.964306 valid_1's auc: 0.904082 [1400] training's auc: 0.966555 valid_1's auc: 0.904213 [1500] training's auc: 0.968699 valid_1's auc: 0.904434 [1600] training's auc: 0.970688 valid_1's auc: 0.904517 [1700] training's auc: 0.972563 valid_1's auc: 0.904712 [1800] training's auc: 0.974206 valid_1's auc: 0.904915 [1900] training's auc: 0.975766 valid_1's auc: 0.905044 [2000] training's auc: 0.977215 valid_1's auc: 0.905078 [2100] training's auc: 0.978628 valid_1's auc: 0.905125 [2200] training's auc: 0.979947 valid_1's auc: 0.905255 Early stopping, best iteration is: [2192] training's auc: 0.979852 valid_1's auc: 0.905273 Partial score of fold 4 is: 0.9052725726386746 Our oof AUC score is: 0.9036389707541601 auc: 0.9036389707541601 | 8 | 0.9036 | 0.8202 | 3.754 | 1.866 | 0.007379 | 16.71 | 9.988 | 1.947 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.914893 valid_1's auc: 0.8901 [200] training's auc: 0.931726 valid_1's auc: 0.897994 [300] training's auc: 0.942469 valid_1's auc: 0.901494 [400] training's auc: 0.951437 valid_1's auc: 0.903327 [500] training's auc: 0.959043 valid_1's auc: 0.904429 [600] training's auc: 0.964981 valid_1's auc: 0.905184 [700] training's auc: 0.970136 valid_1's auc: 0.905463 [800] training's auc: 0.974611 valid_1's auc: 0.905923 [900] training's auc: 0.97837 valid_1's auc: 0.905949 [1000] training's auc: 0.981618 valid_1's auc: 0.906011 [1100] training's auc: 0.984328 valid_1's auc: 0.906062 Early stopping, best iteration is: [1026] training's auc: 0.982329 valid_1's auc: 0.906218 Partial score of fold 0 is: 0.9062182165219057 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.914513 valid_1's auc: 0.888507 [200] training's auc: 0.931208 valid_1's auc: 0.896006 [300] training's auc: 0.942548 valid_1's auc: 0.899966 [400] training's auc: 0.95128 valid_1's auc: 0.901739 [500] training's auc: 0.95903 valid_1's auc: 0.902774 [600] training's auc: 0.965366 valid_1's auc: 0.903556 [700] training's auc: 0.970408 valid_1's auc: 0.903764 [800] training's auc: 0.975129 valid_1's auc: 0.904113 [900] training's auc: 0.979034 valid_1's auc: 0.904003 Early stopping, best iteration is: [863] training's auc: 0.977773 valid_1's auc: 0.904243 Partial score of fold 1 is: 0.904243136728697 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.915726 valid_1's auc: 0.886524 [200] training's auc: 0.932266 valid_1's auc: 0.894821 [300] training's auc: 0.943092 valid_1's auc: 0.897805 [400] training's auc: 0.951911 valid_1's auc: 0.899271 [500] training's auc: 0.959816 valid_1's auc: 0.900163 [600] training's auc: 0.965826 valid_1's auc: 0.900423 [700] training's auc: 0.971173 valid_1's auc: 0.900471 [800] training's auc: 0.975594 valid_1's auc: 0.901163 [900] training's auc: 0.979165 valid_1's auc: 0.901273 [1000] training's auc: 0.98234 valid_1's auc: 0.901282 Early stopping, best iteration is: [934] training's auc: 0.980373 valid_1's auc: 0.901449 Partial score of fold 2 is: 0.9014487536881273 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.914959 valid_1's auc: 0.890688 [200] training's auc: 0.931782 valid_1's auc: 0.897892 [300] training's auc: 0.942911 valid_1's auc: 0.900301 [400] training's auc: 0.952016 valid_1's auc: 0.901938 [500] training's auc: 0.959399 valid_1's auc: 0.903083 [600] training's auc: 0.965773 valid_1's auc: 0.903557 [700] training's auc: 0.971025 valid_1's auc: 0.90356 Early stopping, best iteration is: [681] training's auc: 0.970058 valid_1's auc: 0.903685 Partial score of fold 3 is: 0.9036845289603493 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.914822 valid_1's auc: 0.888179 [200] training's auc: 0.932284 valid_1's auc: 0.897906 [300] training's auc: 0.943444 valid_1's auc: 0.90087 [400] training's auc: 0.95178 valid_1's auc: 0.902373 [500] training's auc: 0.959731 valid_1's auc: 0.903271 [600] training's auc: 0.965527 valid_1's auc: 0.903767 [700] training's auc: 0.971112 valid_1's auc: 0.904001 [800] training's auc: 0.975531 valid_1's auc: 0.904454 [900] training's auc: 0.979218 valid_1's auc: 0.904632 [1000] training's auc: 0.982327 valid_1's auc: 0.904918 [1100] training's auc: 0.984991 valid_1's auc: 0.904862 Early stopping, best iteration is: [1002] training's auc: 0.982398 valid_1's auc: 0.904956 Partial score of fold 4 is: 0.9049563962709131 Our oof AUC score is: 0.9040562573563592 auc: 0.9040562573563592 | 9 | 0.9041 | 0.9569 | 1.553 | 2.381 | 0.01897 | 16.49 | 1.495 | 9.698 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.91541 valid_1's auc: 0.889443 [200] training's auc: 0.92937 valid_1's auc: 0.896249 [300] training's auc: 0.938849 valid_1's auc: 0.899573 [400] training's auc: 0.946334 valid_1's auc: 0.901761 [500] training's auc: 0.952719 valid_1's auc: 0.902951 [600] training's auc: 0.958112 valid_1's auc: 0.9038 [700] training's auc: 0.963004 valid_1's auc: 0.904322 [800] training's auc: 0.967201 valid_1's auc: 0.904759 [900] training's auc: 0.970785 valid_1's auc: 0.905116 [1000] training's auc: 0.974005 valid_1's auc: 0.905272 Early stopping, best iteration is: [960] training's auc: 0.972753 valid_1's auc: 0.905291 Partial score of fold 0 is: 0.9052905121088173 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.915198 valid_1's auc: 0.885519 [200] training's auc: 0.929686 valid_1's auc: 0.892923 [300] training's auc: 0.939402 valid_1's auc: 0.896901 [400] training's auc: 0.946861 valid_1's auc: 0.89886 [500] training's auc: 0.953206 valid_1's auc: 0.900178 [600] training's auc: 0.958677 valid_1's auc: 0.901041 [700] training's auc: 0.963579 valid_1's auc: 0.901805 [800] training's auc: 0.967693 valid_1's auc: 0.902109 [900] training's auc: 0.971357 valid_1's auc: 0.902277 [1000] training's auc: 0.974639 valid_1's auc: 0.902303 [1100] training's auc: 0.977595 valid_1's auc: 0.902444 [1200] training's auc: 0.980126 valid_1's auc: 0.902558 [1300] training's auc: 0.982311 valid_1's auc: 0.90249 Early stopping, best iteration is: [1215] training's auc: 0.980467 valid_1's auc: 0.902595 Partial score of fold 1 is: 0.9025954842396164 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.91607 valid_1's auc: 0.883814 [200] training's auc: 0.930493 valid_1's auc: 0.891104 [300] training's auc: 0.939888 valid_1's auc: 0.894817 [400] training's auc: 0.947366 valid_1's auc: 0.896471 [500] training's auc: 0.953665 valid_1's auc: 0.897773 [600] training's auc: 0.959087 valid_1's auc: 0.898376 [700] training's auc: 0.96379 valid_1's auc: 0.89903 [800] training's auc: 0.968116 valid_1's auc: 0.899405 [900] training's auc: 0.971547 valid_1's auc: 0.89978 [1000] training's auc: 0.974828 valid_1's auc: 0.900108 [1100] training's auc: 0.977536 valid_1's auc: 0.900313 [1200] training's auc: 0.98004 valid_1's auc: 0.900337 [1300] training's auc: 0.982352 valid_1's auc: 0.900584 [1400] training's auc: 0.984328 valid_1's auc: 0.900725 [1500] training's auc: 0.986116 valid_1's auc: 0.900826 [1600] training's auc: 0.987705 valid_1's auc: 0.900924 Early stopping, best iteration is: [1593] training's auc: 0.987601 valid_1's auc: 0.900959 Partial score of fold 2 is: 0.9009594030849856 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.915631 valid_1's auc: 0.889465 [200] training's auc: 0.929756 valid_1's auc: 0.895064 [300] training's auc: 0.939542 valid_1's auc: 0.898338 [400] training's auc: 0.947174 valid_1's auc: 0.900211 [500] training's auc: 0.953504 valid_1's auc: 0.901375 [600] training's auc: 0.959059 valid_1's auc: 0.901944 [700] training's auc: 0.963819 valid_1's auc: 0.902355 [800] training's auc: 0.967936 valid_1's auc: 0.902611 [900] training's auc: 0.971614 valid_1's auc: 0.902996 [1000] training's auc: 0.97475 valid_1's auc: 0.903265 [1100] training's auc: 0.977564 valid_1's auc: 0.903406 [1200] training's auc: 0.979957 valid_1's auc: 0.903391 Early stopping, best iteration is: [1161] training's auc: 0.979043 valid_1's auc: 0.903523 Partial score of fold 3 is: 0.9035227467750332 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.915389 valid_1's auc: 0.884536 [200] training's auc: 0.929332 valid_1's auc: 0.893679 [300] training's auc: 0.938955 valid_1's auc: 0.89829 [400] training's auc: 0.946527 valid_1's auc: 0.900606 [500] training's auc: 0.952832 valid_1's auc: 0.901984 [600] training's auc: 0.958306 valid_1's auc: 0.902908 [700] training's auc: 0.963365 valid_1's auc: 0.903302 [800] training's auc: 0.967488 valid_1's auc: 0.903618 [900] training's auc: 0.971204 valid_1's auc: 0.903794 [1000] training's auc: 0.974464 valid_1's auc: 0.903908 [1100] training's auc: 0.977122 valid_1's auc: 0.904231 [1200] training's auc: 0.979614 valid_1's auc: 0.904263 [1300] training's auc: 0.981997 valid_1's auc: 0.904211 Early stopping, best iteration is: [1203] training's auc: 0.979688 valid_1's auc: 0.90429 Partial score of fold 4 is: 0.9042903451957645 Our oof AUC score is: 0.903116469188148 auc: 0.903116469188148 | 10 | 0.9031 | 0.9777 | 0.3474 | 2.403 | 0.01265 | 14.47 | 4.909 | 3.499 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.910315 valid_1's auc: 0.888552 [200] training's auc: 0.920593 valid_1's auc: 0.893777 [300] training's auc: 0.928667 valid_1's auc: 0.897441 [400] training's auc: 0.935023 valid_1's auc: 0.899898 [500] training's auc: 0.940483 valid_1's auc: 0.901491 [600] training's auc: 0.945197 valid_1's auc: 0.902627 [700] training's auc: 0.949638 valid_1's auc: 0.903415 [800] training's auc: 0.953728 valid_1's auc: 0.90426 [900] training's auc: 0.95746 valid_1's auc: 0.904602 [1000] training's auc: 0.960782 valid_1's auc: 0.90512 [1100] training's auc: 0.963804 valid_1's auc: 0.905482 [1200] training's auc: 0.96674 valid_1's auc: 0.905799 [1300] training's auc: 0.969334 valid_1's auc: 0.906042 [1400] training's auc: 0.971683 valid_1's auc: 0.906179 [1500] training's auc: 0.973885 valid_1's auc: 0.906423 [1600] training's auc: 0.975789 valid_1's auc: 0.906702 [1700] training's auc: 0.977575 valid_1's auc: 0.90675 [1800] training's auc: 0.979286 valid_1's auc: 0.906935 [1900] training's auc: 0.980887 valid_1's auc: 0.907133 [2000] training's auc: 0.982327 valid_1's auc: 0.907216 [2100] training's auc: 0.983594 valid_1's auc: 0.907099 Early stopping, best iteration is: [2033] training's auc: 0.982766 valid_1's auc: 0.907308 Partial score of fold 0 is: 0.9073082211504394 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.910845 valid_1's auc: 0.886555 [200] training's auc: 0.920825 valid_1's auc: 0.89216 [300] training's auc: 0.928994 valid_1's auc: 0.896389 [400] training's auc: 0.935411 valid_1's auc: 0.899008 [500] training's auc: 0.940917 valid_1's auc: 0.900697 [600] training's auc: 0.945802 valid_1's auc: 0.90208 [700] training's auc: 0.950168 valid_1's auc: 0.902999 [800] training's auc: 0.954135 valid_1's auc: 0.903665 [900] training's auc: 0.957883 valid_1's auc: 0.904179 [1000] training's auc: 0.961426 valid_1's auc: 0.904448 [1100] training's auc: 0.964517 valid_1's auc: 0.90469 [1200] training's auc: 0.967323 valid_1's auc: 0.904838 [1300] training's auc: 0.969826 valid_1's auc: 0.905007 [1400] training's auc: 0.972026 valid_1's auc: 0.905135 Early stopping, best iteration is: [1349] training's auc: 0.970877 valid_1's auc: 0.905224 Partial score of fold 1 is: 0.9052239863586404 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.910641 valid_1's auc: 0.883975 [200] training's auc: 0.921155 valid_1's auc: 0.889613 [300] training's auc: 0.929484 valid_1's auc: 0.894077 [400] training's auc: 0.935825 valid_1's auc: 0.896776 [500] training's auc: 0.941349 valid_1's auc: 0.898639 [600] training's auc: 0.946117 valid_1's auc: 0.899645 [700] training's auc: 0.950534 valid_1's auc: 0.900386 [800] training's auc: 0.954484 valid_1's auc: 0.901043 [900] training's auc: 0.958145 valid_1's auc: 0.901469 [1000] training's auc: 0.961581 valid_1's auc: 0.90185 [1100] training's auc: 0.964687 valid_1's auc: 0.902045 [1200] training's auc: 0.967449 valid_1's auc: 0.902316 [1300] training's auc: 0.969931 valid_1's auc: 0.902669 [1400] training's auc: 0.972262 valid_1's auc: 0.90279 [1500] training's auc: 0.974319 valid_1's auc: 0.902882 [1600] training's auc: 0.976306 valid_1's auc: 0.902871 Early stopping, best iteration is: [1538] training's auc: 0.975058 valid_1's auc: 0.903002 Partial score of fold 2 is: 0.903002440910811 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.910836 valid_1's auc: 0.88858 [200] training's auc: 0.920756 valid_1's auc: 0.893269 [300] training's auc: 0.929003 valid_1's auc: 0.896853 [400] training's auc: 0.935566 valid_1's auc: 0.899236 [500] training's auc: 0.940943 valid_1's auc: 0.900998 [600] training's auc: 0.945845 valid_1's auc: 0.902211 [700] training's auc: 0.950259 valid_1's auc: 0.902989 [800] training's auc: 0.954392 valid_1's auc: 0.903522 [900] training's auc: 0.958166 valid_1's auc: 0.903919 [1000] training's auc: 0.961513 valid_1's auc: 0.904501 [1100] training's auc: 0.964553 valid_1's auc: 0.904938 [1200] training's auc: 0.967447 valid_1's auc: 0.905102 [1300] training's auc: 0.970023 valid_1's auc: 0.90519 [1400] training's auc: 0.972253 valid_1's auc: 0.905366 [1500] training's auc: 0.97442 valid_1's auc: 0.905362 Early stopping, best iteration is: [1435] training's auc: 0.972989 valid_1's auc: 0.905431 Partial score of fold 3 is: 0.9054313016187228 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.910445 valid_1's auc: 0.885466 [200] training's auc: 0.920701 valid_1's auc: 0.891591 [300] training's auc: 0.928756 valid_1's auc: 0.896678 [400] training's auc: 0.935218 valid_1's auc: 0.899864 [500] training's auc: 0.940694 valid_1's auc: 0.901974 [600] training's auc: 0.945588 valid_1's auc: 0.903403 [700] training's auc: 0.950237 valid_1's auc: 0.904369 [800] training's auc: 0.954173 valid_1's auc: 0.90507 [900] training's auc: 0.957824 valid_1's auc: 0.905573 [1000] training's auc: 0.961264 valid_1's auc: 0.906121 [1100] training's auc: 0.964253 valid_1's auc: 0.906361 [1200] training's auc: 0.967043 valid_1's auc: 0.906676 [1300] training's auc: 0.969572 valid_1's auc: 0.906917 [1400] training's auc: 0.971956 valid_1's auc: 0.907019 Early stopping, best iteration is: [1388] training's auc: 0.971657 valid_1's auc: 0.907051 Partial score of fold 4 is: 0.9070511212481713 Our oof AUC score is: 0.9054503552499539 auc: 0.9054503552499539 | 11 | 0.9055 | 0.6576 | 3.07 | 1.994 | 0.009464 | 15.7 | 5.839 | 9.55 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.92156 valid_1's auc: 0.893148 [200] training's auc: 0.93646 valid_1's auc: 0.899328 [300] training's auc: 0.946846 valid_1's auc: 0.902223 [400] training's auc: 0.955245 valid_1's auc: 0.903553 [500] training's auc: 0.962122 valid_1's auc: 0.904034 [600] training's auc: 0.967964 valid_1's auc: 0.904759 [700] training's auc: 0.972879 valid_1's auc: 0.905053 Early stopping, best iteration is: [690] training's auc: 0.972443 valid_1's auc: 0.905176 Partial score of fold 0 is: 0.905176193355114 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.921676 valid_1's auc: 0.891186 [200] training's auc: 0.936929 valid_1's auc: 0.898561 [300] training's auc: 0.94722 valid_1's auc: 0.901891 [400] training's auc: 0.955637 valid_1's auc: 0.902909 [500] training's auc: 0.962457 valid_1's auc: 0.903862 [600] training's auc: 0.968234 valid_1's auc: 0.904396 [700] training's auc: 0.972772 valid_1's auc: 0.904468 [800] training's auc: 0.97704 valid_1's auc: 0.904598 [900] training's auc: 0.980582 valid_1's auc: 0.904695 [1000] training's auc: 0.983389 valid_1's auc: 0.904433 Early stopping, best iteration is: [908] training's auc: 0.980846 valid_1's auc: 0.904723 Partial score of fold 1 is: 0.9047227628879035 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.921915 valid_1's auc: 0.888279 [200] training's auc: 0.936733 valid_1's auc: 0.895282 [300] training's auc: 0.947401 valid_1's auc: 0.898367 [400] training's auc: 0.955936 valid_1's auc: 0.899591 [500] training's auc: 0.962766 valid_1's auc: 0.900809 [600] training's auc: 0.968481 valid_1's auc: 0.901337 [700] training's auc: 0.973332 valid_1's auc: 0.901572 [800] training's auc: 0.977394 valid_1's auc: 0.901972 [900] training's auc: 0.980667 valid_1's auc: 0.9021 Early stopping, best iteration is: [885] training's auc: 0.980211 valid_1's auc: 0.902184 Partial score of fold 2 is: 0.9021840422628857 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.921221 valid_1's auc: 0.891874 [200] training's auc: 0.936419 valid_1's auc: 0.898581 [300] training's auc: 0.946881 valid_1's auc: 0.901226 [400] training's auc: 0.955322 valid_1's auc: 0.902574 [500] training's auc: 0.962144 valid_1's auc: 0.90368 [600] training's auc: 0.967984 valid_1's auc: 0.904375 [700] training's auc: 0.972829 valid_1's auc: 0.904719 Early stopping, best iteration is: [688] training's auc: 0.97225 valid_1's auc: 0.904823 Partial score of fold 3 is: 0.9048233745253443 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.921305 valid_1's auc: 0.889549 [200] training's auc: 0.936558 valid_1's auc: 0.89923 [300] training's auc: 0.946907 valid_1's auc: 0.902381 [400] training's auc: 0.955223 valid_1's auc: 0.903971 [500] training's auc: 0.962193 valid_1's auc: 0.905011 [600] training's auc: 0.96809 valid_1's auc: 0.905301 [700] training's auc: 0.97265 valid_1's auc: 0.905721 [800] training's auc: 0.976832 valid_1's auc: 0.905863 [900] training's auc: 0.980377 valid_1's auc: 0.905773 Early stopping, best iteration is: [803] training's auc: 0.976951 valid_1's auc: 0.905872 Partial score of fold 4 is: 0.9058716416673862 Our oof AUC score is: 0.9044347688859878 auc: 0.9044347688859878 | 12 | 0.9044 | 0.7344 | 1.269 | 0.9706 | 0.01855 | 14.4 | 7.089 | 5.414 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.91091 valid_1's auc: 0.886528 [200] training's auc: 0.921765 valid_1's auc: 0.891898 [300] training's auc: 0.930413 valid_1's auc: 0.896194 [400] training's auc: 0.937092 valid_1's auc: 0.898555 [500] training's auc: 0.942633 valid_1's auc: 0.900158 [600] training's auc: 0.947535 valid_1's auc: 0.901477 [700] training's auc: 0.951977 valid_1's auc: 0.902387 [800] training's auc: 0.955899 valid_1's auc: 0.9032 [900] training's auc: 0.959617 valid_1's auc: 0.903793 [1000] training's auc: 0.962883 valid_1's auc: 0.904234 [1100] training's auc: 0.965893 valid_1's auc: 0.904569 [1200] training's auc: 0.968629 valid_1's auc: 0.904881 [1300] training's auc: 0.971091 valid_1's auc: 0.905082 [1400] training's auc: 0.973324 valid_1's auc: 0.905123 [1500] training's auc: 0.975468 valid_1's auc: 0.905302 [1600] training's auc: 0.977396 valid_1's auc: 0.905474 [1700] training's auc: 0.979134 valid_1's auc: 0.905489 Early stopping, best iteration is: [1653] training's auc: 0.978372 valid_1's auc: 0.905568 Partial score of fold 0 is: 0.9055681487523314 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.911245 valid_1's auc: 0.883818 [200] training's auc: 0.922259 valid_1's auc: 0.889493 [300] training's auc: 0.931077 valid_1's auc: 0.894366 [400] training's auc: 0.937778 valid_1's auc: 0.897091 [500] training's auc: 0.943292 valid_1's auc: 0.898832 [600] training's auc: 0.948251 valid_1's auc: 0.900081 [700] training's auc: 0.952566 valid_1's auc: 0.901138 [800] training's auc: 0.956482 valid_1's auc: 0.901893 [900] training's auc: 0.960042 valid_1's auc: 0.902439 [1000] training's auc: 0.963365 valid_1's auc: 0.90266 [1100] training's auc: 0.96631 valid_1's auc: 0.902778 [1200] training's auc: 0.969102 valid_1's auc: 0.903099 [1300] training's auc: 0.971562 valid_1's auc: 0.903092 [1400] training's auc: 0.973751 valid_1's auc: 0.90331 [1500] training's auc: 0.975911 valid_1's auc: 0.903495 [1600] training's auc: 0.977827 valid_1's auc: 0.903582 [1700] training's auc: 0.979611 valid_1's auc: 0.903671 [1800] training's auc: 0.981229 valid_1's auc: 0.903803 [1900] training's auc: 0.982714 valid_1's auc: 0.903852 [2000] training's auc: 0.984088 valid_1's auc: 0.903828 Early stopping, best iteration is: [1939] training's auc: 0.983263 valid_1's auc: 0.90394 Partial score of fold 1 is: 0.9039395682346892 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.91199 valid_1's auc: 0.881638 [200] training's auc: 0.92297 valid_1's auc: 0.886962 [300] training's auc: 0.931402 valid_1's auc: 0.89151 [400] training's auc: 0.938097 valid_1's auc: 0.89415 [500] training's auc: 0.943635 valid_1's auc: 0.896153 [600] training's auc: 0.948505 valid_1's auc: 0.897523 [700] training's auc: 0.952914 valid_1's auc: 0.898407 [800] training's auc: 0.956859 valid_1's auc: 0.899119 [900] training's auc: 0.960303 valid_1's auc: 0.899607 [1000] training's auc: 0.963643 valid_1's auc: 0.899814 [1100] training's auc: 0.966629 valid_1's auc: 0.900021 [1200] training's auc: 0.969333 valid_1's auc: 0.900208 [1300] training's auc: 0.971762 valid_1's auc: 0.900453 [1400] training's auc: 0.973984 valid_1's auc: 0.900733 [1500] training's auc: 0.976099 valid_1's auc: 0.900817 [1600] training's auc: 0.978011 valid_1's auc: 0.900898 [1700] training's auc: 0.979755 valid_1's auc: 0.901008 [1800] training's auc: 0.981375 valid_1's auc: 0.90094 Early stopping, best iteration is: [1718] training's auc: 0.980062 valid_1's auc: 0.901016 Partial score of fold 2 is: 0.9010158274747955 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.911163 valid_1's auc: 0.887642 [200] training's auc: 0.922136 valid_1's auc: 0.891636 [300] training's auc: 0.930774 valid_1's auc: 0.895203 [400] training's auc: 0.937562 valid_1's auc: 0.897651 [500] training's auc: 0.943279 valid_1's auc: 0.899401 [600] training's auc: 0.948177 valid_1's auc: 0.900646 [700] training's auc: 0.952489 valid_1's auc: 0.901432 [800] training's auc: 0.956565 valid_1's auc: 0.902035 [900] training's auc: 0.960158 valid_1's auc: 0.90262 [1000] training's auc: 0.963419 valid_1's auc: 0.903139 [1100] training's auc: 0.966375 valid_1's auc: 0.903583 [1200] training's auc: 0.969135 valid_1's auc: 0.90376 Early stopping, best iteration is: [1193] training's auc: 0.968978 valid_1's auc: 0.903805 Partial score of fold 3 is: 0.9038046593947862 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.910948 valid_1's auc: 0.881781 [200] training's auc: 0.921938 valid_1's auc: 0.888547 [300] training's auc: 0.930597 valid_1's auc: 0.893936 [400] training's auc: 0.937334 valid_1's auc: 0.897473 [500] training's auc: 0.943007 valid_1's auc: 0.899477 [600] training's auc: 0.947957 valid_1's auc: 0.900848 [700] training's auc: 0.952413 valid_1's auc: 0.901833 [800] training's auc: 0.956366 valid_1's auc: 0.902575 [900] training's auc: 0.959941 valid_1's auc: 0.903092 [1000] training's auc: 0.963142 valid_1's auc: 0.903477 [1100] training's auc: 0.96613 valid_1's auc: 0.90371 [1200] training's auc: 0.9688 valid_1's auc: 0.904003 [1300] training's auc: 0.971296 valid_1's auc: 0.904169 [1400] training's auc: 0.973597 valid_1's auc: 0.904325 Early stopping, best iteration is: [1368] training's auc: 0.972893 valid_1's auc: 0.904384 Partial score of fold 4 is: 0.9043840145178779 Our oof AUC score is: 0.9035179912612705 auc: 0.9035179912612705 | 13 | 0.9035 | 0.853 | 1.536 | 0.4828 | 0.008905 | 14.28 | 7.365 | 3.389 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.907685 valid_1's auc: 0.890305 [200] training's auc: 0.919754 valid_1's auc: 0.895815 [300] training's auc: 0.928694 valid_1's auc: 0.899999 [400] training's auc: 0.935669 valid_1's auc: 0.902551 [500] training's auc: 0.94158 valid_1's auc: 0.904156 [600] training's auc: 0.947046 valid_1's auc: 0.905534 [700] training's auc: 0.952092 valid_1's auc: 0.906383 [800] training's auc: 0.95666 valid_1's auc: 0.907206 [900] training's auc: 0.960705 valid_1's auc: 0.907687 [1000] training's auc: 0.964291 valid_1's auc: 0.908053 [1100] training's auc: 0.967646 valid_1's auc: 0.908322 [1200] training's auc: 0.970728 valid_1's auc: 0.908579 [1300] training's auc: 0.973502 valid_1's auc: 0.908796 [1400] training's auc: 0.97593 valid_1's auc: 0.909007 [1500] training's auc: 0.978109 valid_1's auc: 0.908999 [1600] training's auc: 0.980117 valid_1's auc: 0.909053 Early stopping, best iteration is: [1592] training's auc: 0.979984 valid_1's auc: 0.909108 Partial score of fold 0 is: 0.9091081478778852 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.907705 valid_1's auc: 0.887974 [200] training's auc: 0.919669 valid_1's auc: 0.893795 [300] training's auc: 0.928798 valid_1's auc: 0.898481 [400] training's auc: 0.935728 valid_1's auc: 0.901182 [500] training's auc: 0.941731 valid_1's auc: 0.902988 [600] training's auc: 0.9472 valid_1's auc: 0.904244 [700] training's auc: 0.952172 valid_1's auc: 0.904956 [800] training's auc: 0.956668 valid_1's auc: 0.905444 [900] training's auc: 0.960849 valid_1's auc: 0.905833 [1000] training's auc: 0.964426 valid_1's auc: 0.906193 [1100] training's auc: 0.967796 valid_1's auc: 0.906432 [1200] training's auc: 0.970925 valid_1's auc: 0.906771 [1300] training's auc: 0.973655 valid_1's auc: 0.906893 [1400] training's auc: 0.97609 valid_1's auc: 0.907084 [1500] training's auc: 0.978382 valid_1's auc: 0.907038 Early stopping, best iteration is: [1440] training's auc: 0.977033 valid_1's auc: 0.907127 Partial score of fold 1 is: 0.9071267358886258 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.908889 valid_1's auc: 0.884628 [200] training's auc: 0.920462 valid_1's auc: 0.890885 [300] training's auc: 0.929438 valid_1's auc: 0.895515 [400] training's auc: 0.936501 valid_1's auc: 0.898009 [500] training's auc: 0.942618 valid_1's auc: 0.89955 [600] training's auc: 0.948077 valid_1's auc: 0.900457 [700] training's auc: 0.952987 valid_1's auc: 0.901234 [800] training's auc: 0.957445 valid_1's auc: 0.901937 [900] training's auc: 0.961532 valid_1's auc: 0.902318 [1000] training's auc: 0.965037 valid_1's auc: 0.902687 [1100] training's auc: 0.968318 valid_1's auc: 0.902993 [1200] training's auc: 0.971315 valid_1's auc: 0.90314 [1300] training's auc: 0.974016 valid_1's auc: 0.903286 [1400] training's auc: 0.976456 valid_1's auc: 0.90343 [1500] training's auc: 0.978675 valid_1's auc: 0.903564 [1600] training's auc: 0.980739 valid_1's auc: 0.903652 [1700] training's auc: 0.982591 valid_1's auc: 0.903816 [1800] training's auc: 0.984265 valid_1's auc: 0.90379 Early stopping, best iteration is: [1717] training's auc: 0.982886 valid_1's auc: 0.90385 Partial score of fold 2 is: 0.903849522899181 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.908164 valid_1's auc: 0.890754 [200] training's auc: 0.919764 valid_1's auc: 0.895874 [300] training's auc: 0.929093 valid_1's auc: 0.899549 [400] training's auc: 0.936238 valid_1's auc: 0.901919 [500] training's auc: 0.942251 valid_1's auc: 0.90308 [600] training's auc: 0.94763 valid_1's auc: 0.903757 [700] training's auc: 0.952635 valid_1's auc: 0.90447 [800] training's auc: 0.957173 valid_1's auc: 0.905023 [900] training's auc: 0.961428 valid_1's auc: 0.905372 [1000] training's auc: 0.964922 valid_1's auc: 0.905712 [1100] training's auc: 0.968182 valid_1's auc: 0.905957 [1200] training's auc: 0.971173 valid_1's auc: 0.906114 Early stopping, best iteration is: [1185] training's auc: 0.970722 valid_1's auc: 0.906145 Partial score of fold 3 is: 0.9061450354675022 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.90763 valid_1's auc: 0.888399 [200] training's auc: 0.919563 valid_1's auc: 0.894806 [300] training's auc: 0.928634 valid_1's auc: 0.899939 [400] training's auc: 0.935809 valid_1's auc: 0.902952 [500] training's auc: 0.94191 valid_1's auc: 0.904626 [600] training's auc: 0.947339 valid_1's auc: 0.905661 [700] training's auc: 0.952259 valid_1's auc: 0.906229 [800] training's auc: 0.956797 valid_1's auc: 0.906929 [900] training's auc: 0.961012 valid_1's auc: 0.907384 [1000] training's auc: 0.964664 valid_1's auc: 0.907709 [1100] training's auc: 0.967961 valid_1's auc: 0.90796 [1200] training's auc: 0.971058 valid_1's auc: 0.908108 [1300] training's auc: 0.9738 valid_1's auc: 0.908131 [1400] training's auc: 0.976283 valid_1's auc: 0.908282 [1500] training's auc: 0.978477 valid_1's auc: 0.908281 [1600] training's auc: 0.980524 valid_1's auc: 0.908308 [1700] training's auc: 0.982439 valid_1's auc: 0.908441 Early stopping, best iteration is: [1699] training's auc: 0.982424 valid_1's auc: 0.908451 Partial score of fold 4 is: 0.9084505069960429 Our oof AUC score is: 0.9068646608695636 auc: 0.9068646608695636 | 14 | 0.9069 | 0.6826 | 1.33 | 2.889 | 0.01166 | 16.0 | 1.74 | 2.542 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.917446 valid_1's auc: 0.891603 [200] training's auc: 0.930516 valid_1's auc: 0.898161 [300] training's auc: 0.940072 valid_1's auc: 0.901545 [400] training's auc: 0.948071 valid_1's auc: 0.90341 [500] training's auc: 0.954605 valid_1's auc: 0.904575 [600] training's auc: 0.959994 valid_1's auc: 0.905486 [700] training's auc: 0.964925 valid_1's auc: 0.906143 [800] training's auc: 0.969335 valid_1's auc: 0.906557 [900] training's auc: 0.973007 valid_1's auc: 0.906638 [1000] training's auc: 0.97631 valid_1's auc: 0.906799 [1100] training's auc: 0.979197 valid_1's auc: 0.907005 [1200] training's auc: 0.981709 valid_1's auc: 0.907133 [1300] training's auc: 0.983932 valid_1's auc: 0.907073 Early stopping, best iteration is: [1232] training's auc: 0.982428 valid_1's auc: 0.907161 Partial score of fold 0 is: 0.9071612237421168 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.917772 valid_1's auc: 0.89061 [200] training's auc: 0.930722 valid_1's auc: 0.897248 [300] training's auc: 0.940642 valid_1's auc: 0.90109 [400] training's auc: 0.948541 valid_1's auc: 0.903044 [500] training's auc: 0.955201 valid_1's auc: 0.904251 [600] training's auc: 0.960987 valid_1's auc: 0.905027 [700] training's auc: 0.96578 valid_1's auc: 0.905319 [800] training's auc: 0.970037 valid_1's auc: 0.905575 [900] training's auc: 0.973772 valid_1's auc: 0.905697 Early stopping, best iteration is: [873] training's auc: 0.9728 valid_1's auc: 0.90584 Partial score of fold 1 is: 0.9058399054995124 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.918164 valid_1's auc: 0.887158 [200] training's auc: 0.931542 valid_1's auc: 0.894782 [300] training's auc: 0.941054 valid_1's auc: 0.898573 [400] training's auc: 0.948889 valid_1's auc: 0.900304 [500] training's auc: 0.955421 valid_1's auc: 0.90142 [600] training's auc: 0.96078 valid_1's auc: 0.902278 [700] training's auc: 0.965827 valid_1's auc: 0.90227 Early stopping, best iteration is: [605] training's auc: 0.96103 valid_1's auc: 0.902331 Partial score of fold 2 is: 0.9023313035127103 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.917722 valid_1's auc: 0.890971 [200] training's auc: 0.930803 valid_1's auc: 0.897052 [300] training's auc: 0.940596 valid_1's auc: 0.90043 [400] training's auc: 0.948581 valid_1's auc: 0.90192 [500] training's auc: 0.954998 valid_1's auc: 0.903129 [600] training's auc: 0.960751 valid_1's auc: 0.903675 [700] training's auc: 0.965587 valid_1's auc: 0.904204 [800] training's auc: 0.969934 valid_1's auc: 0.904306 [900] training's auc: 0.973566 valid_1's auc: 0.904175 Early stopping, best iteration is: [804] training's auc: 0.970092 valid_1's auc: 0.904349 Partial score of fold 3 is: 0.9043488838103488 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.917678 valid_1's auc: 0.889404 [200] training's auc: 0.930807 valid_1's auc: 0.897689 [300] training's auc: 0.940619 valid_1's auc: 0.901919 [400] training's auc: 0.948251 valid_1's auc: 0.904054 [500] training's auc: 0.955 valid_1's auc: 0.905318 [600] training's auc: 0.960607 valid_1's auc: 0.90644 [700] training's auc: 0.965723 valid_1's auc: 0.907052 [800] training's auc: 0.969917 valid_1's auc: 0.907207 [900] training's auc: 0.973634 valid_1's auc: 0.907356 [1000] training's auc: 0.976784 valid_1's auc: 0.907542 [1100] training's auc: 0.979519 valid_1's auc: 0.907441 Early stopping, best iteration is: [1044] training's auc: 0.97801 valid_1's auc: 0.907665 Partial score of fold 4 is: 0.9076650416704095 Our oof AUC score is: 0.9052672026616605 auc: 0.9052672026616605 | 15 | 0.9053 | 0.5689 | 2.251 | 2.164 | 0.01598 | 16.22 | 9.151 | 9.468 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.903261 valid_1's auc: 0.888553 [200] training's auc: 0.910423 valid_1's auc: 0.891666 [300] training's auc: 0.916288 valid_1's auc: 0.894609 [400] training's auc: 0.921216 valid_1's auc: 0.896729 [500] training's auc: 0.925477 valid_1's auc: 0.898764 [600] training's auc: 0.929271 valid_1's auc: 0.900324 [700] training's auc: 0.932635 valid_1's auc: 0.901602 [800] training's auc: 0.935645 valid_1's auc: 0.902739 [900] training's auc: 0.938496 valid_1's auc: 0.903507 [1000] training's auc: 0.941194 valid_1's auc: 0.904085 [1100] training's auc: 0.943751 valid_1's auc: 0.904657 [1200] training's auc: 0.946141 valid_1's auc: 0.905193 [1300] training's auc: 0.94853 valid_1's auc: 0.905692 [1400] training's auc: 0.950737 valid_1's auc: 0.90608 [1500] training's auc: 0.952918 valid_1's auc: 0.906484 [1600] training's auc: 0.955004 valid_1's auc: 0.906848 [1700] training's auc: 0.95694 valid_1's auc: 0.90705 [1800] training's auc: 0.958811 valid_1's auc: 0.907276 [1900] training's auc: 0.960548 valid_1's auc: 0.90748 [2000] training's auc: 0.962166 valid_1's auc: 0.907608 [2100] training's auc: 0.963742 valid_1's auc: 0.907744 [2200] training's auc: 0.965268 valid_1's auc: 0.907859 [2300] training's auc: 0.966716 valid_1's auc: 0.907975 [2400] training's auc: 0.968127 valid_1's auc: 0.908087 [2500] training's auc: 0.969494 valid_1's auc: 0.9081 [2600] training's auc: 0.97078 valid_1's auc: 0.908311 [2700] training's auc: 0.97198 valid_1's auc: 0.908349 [2800] training's auc: 0.973128 valid_1's auc: 0.908432 [2900] training's auc: 0.974239 valid_1's auc: 0.908512 [3000] training's auc: 0.975317 valid_1's auc: 0.908587 [3100] training's auc: 0.976372 valid_1's auc: 0.908599 [3200] training's auc: 0.977333 valid_1's auc: 0.908634 [3300] training's auc: 0.978237 valid_1's auc: 0.908726 [3400] training's auc: 0.979146 valid_1's auc: 0.908785 [3500] training's auc: 0.980012 valid_1's auc: 0.908753 Early stopping, best iteration is: [3401] training's auc: 0.979156 valid_1's auc: 0.908787 Partial score of fold 0 is: 0.9087873166113102 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.903946 valid_1's auc: 0.885243 [200] training's auc: 0.911245 valid_1's auc: 0.889462 [300] training's auc: 0.91699 valid_1's auc: 0.892316 [400] training's auc: 0.921977 valid_1's auc: 0.894851 [500] training's auc: 0.926229 valid_1's auc: 0.897125 [600] training's auc: 0.929881 valid_1's auc: 0.898786 [700] training's auc: 0.93318 valid_1's auc: 0.900133 [800] training's auc: 0.936222 valid_1's auc: 0.901296 [900] training's auc: 0.939144 valid_1's auc: 0.902175 [1000] training's auc: 0.941823 valid_1's auc: 0.902901 [1100] training's auc: 0.944378 valid_1's auc: 0.903497 [1200] training's auc: 0.946805 valid_1's auc: 0.904042 [1300] training's auc: 0.94908 valid_1's auc: 0.904541 [1400] training's auc: 0.951256 valid_1's auc: 0.90496 [1500] training's auc: 0.953447 valid_1's auc: 0.905147 [1600] training's auc: 0.955526 valid_1's auc: 0.905393 [1700] training's auc: 0.957479 valid_1's auc: 0.905668 [1800] training's auc: 0.959258 valid_1's auc: 0.905861 [1900] training's auc: 0.961048 valid_1's auc: 0.906135 [2000] training's auc: 0.962725 valid_1's auc: 0.906353 [2100] training's auc: 0.964304 valid_1's auc: 0.906525 [2200] training's auc: 0.965856 valid_1's auc: 0.906763 [2300] training's auc: 0.967269 valid_1's auc: 0.906772 [2400] training's auc: 0.968692 valid_1's auc: 0.90686 [2500] training's auc: 0.970008 valid_1's auc: 0.906933 [2600] training's auc: 0.971216 valid_1's auc: 0.907036 [2700] training's auc: 0.972498 valid_1's auc: 0.90706 [2800] training's auc: 0.973644 valid_1's auc: 0.907049 Early stopping, best iteration is: [2759] training's auc: 0.97315 valid_1's auc: 0.907092 Partial score of fold 1 is: 0.907091871118703 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.904339 valid_1's auc: 0.882637 [200] training's auc: 0.911377 valid_1's auc: 0.886062 [300] training's auc: 0.917103 valid_1's auc: 0.888933 [400] training's auc: 0.922175 valid_1's auc: 0.891605 [500] training's auc: 0.926418 valid_1's auc: 0.893983 [600] training's auc: 0.930194 valid_1's auc: 0.895763 [700] training's auc: 0.933566 valid_1's auc: 0.897103 [800] training's auc: 0.936584 valid_1's auc: 0.898329 [900] training's auc: 0.939448 valid_1's auc: 0.899206 [1000] training's auc: 0.942148 valid_1's auc: 0.899937 [1100] training's auc: 0.944745 valid_1's auc: 0.900517 [1200] training's auc: 0.947266 valid_1's auc: 0.90097 [1300] training's auc: 0.949557 valid_1's auc: 0.901442 [1400] training's auc: 0.951742 valid_1's auc: 0.901775 [1500] training's auc: 0.953875 valid_1's auc: 0.902017 [1600] training's auc: 0.955925 valid_1's auc: 0.902304 [1700] training's auc: 0.957826 valid_1's auc: 0.902542 [1800] training's auc: 0.95971 valid_1's auc: 0.902763 [1900] training's auc: 0.961415 valid_1's auc: 0.902981 [2000] training's auc: 0.963112 valid_1's auc: 0.903104 [2100] training's auc: 0.964675 valid_1's auc: 0.903234 [2200] training's auc: 0.96621 valid_1's auc: 0.903401 [2300] training's auc: 0.967646 valid_1's auc: 0.903497 [2400] training's auc: 0.968983 valid_1's auc: 0.903602 [2500] training's auc: 0.970252 valid_1's auc: 0.903682 [2600] training's auc: 0.971496 valid_1's auc: 0.903742 [2700] training's auc: 0.972724 valid_1's auc: 0.903826 [2800] training's auc: 0.973856 valid_1's auc: 0.903949 [2900] training's auc: 0.974914 valid_1's auc: 0.904069 [3000] training's auc: 0.975965 valid_1's auc: 0.904116 [3100] training's auc: 0.976999 valid_1's auc: 0.904034 Early stopping, best iteration is: [3001] training's auc: 0.975979 valid_1's auc: 0.90412 Partial score of fold 2 is: 0.9041202619719966 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.90378 valid_1's auc: 0.887384 [200] training's auc: 0.910931 valid_1's auc: 0.890954 [300] training's auc: 0.9167 valid_1's auc: 0.89352 [400] training's auc: 0.921639 valid_1's auc: 0.895879 [500] training's auc: 0.926092 valid_1's auc: 0.897925 [600] training's auc: 0.929953 valid_1's auc: 0.899528 [700] training's auc: 0.933299 valid_1's auc: 0.900542 [800] training's auc: 0.936413 valid_1's auc: 0.901575 [900] training's auc: 0.939345 valid_1's auc: 0.902353 [1000] training's auc: 0.942052 valid_1's auc: 0.902983 [1100] training's auc: 0.944699 valid_1's auc: 0.903423 [1200] training's auc: 0.947196 valid_1's auc: 0.903915 [1300] training's auc: 0.94945 valid_1's auc: 0.904324 [1400] training's auc: 0.951658 valid_1's auc: 0.904656 [1500] training's auc: 0.953795 valid_1's auc: 0.904901 [1600] training's auc: 0.955824 valid_1's auc: 0.905105 [1700] training's auc: 0.957744 valid_1's auc: 0.90537 [1800] training's auc: 0.959568 valid_1's auc: 0.905539 [1900] training's auc: 0.961297 valid_1's auc: 0.905709 [2000] training's auc: 0.962979 valid_1's auc: 0.905846 [2100] training's auc: 0.964532 valid_1's auc: 0.90599 [2200] training's auc: 0.966011 valid_1's auc: 0.906046 [2300] training's auc: 0.967435 valid_1's auc: 0.906135 [2400] training's auc: 0.968822 valid_1's auc: 0.906156 Early stopping, best iteration is: [2347] training's auc: 0.968086 valid_1's auc: 0.9062 Partial score of fold 3 is: 0.9061996916111904 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.903971 valid_1's auc: 0.884625 [200] training's auc: 0.911049 valid_1's auc: 0.888119 [300] training's auc: 0.916762 valid_1's auc: 0.891722 [400] training's auc: 0.921667 valid_1's auc: 0.894725 [500] training's auc: 0.925957 valid_1's auc: 0.897742 [600] training's auc: 0.929701 valid_1's auc: 0.89978 [700] training's auc: 0.93308 valid_1's auc: 0.901282 [800] training's auc: 0.936171 valid_1's auc: 0.902531 [900] training's auc: 0.939023 valid_1's auc: 0.903475 [1000] training's auc: 0.941683 valid_1's auc: 0.904301 [1100] training's auc: 0.944283 valid_1's auc: 0.904894 [1200] training's auc: 0.946706 valid_1's auc: 0.905428 [1300] training's auc: 0.949011 valid_1's auc: 0.905775 [1400] training's auc: 0.951233 valid_1's auc: 0.906064 [1500] training's auc: 0.953355 valid_1's auc: 0.906351 [1600] training's auc: 0.955408 valid_1's auc: 0.906662 [1700] training's auc: 0.957367 valid_1's auc: 0.906905 [1800] training's auc: 0.959257 valid_1's auc: 0.90712 [1900] training's auc: 0.961001 valid_1's auc: 0.90727 [2000] training's auc: 0.962709 valid_1's auc: 0.907512 [2100] training's auc: 0.964283 valid_1's auc: 0.907577 [2200] training's auc: 0.965776 valid_1's auc: 0.907738 [2300] training's auc: 0.967193 valid_1's auc: 0.907783 [2400] training's auc: 0.968605 valid_1's auc: 0.907869 [2500] training's auc: 0.969962 valid_1's auc: 0.907937 [2600] training's auc: 0.971237 valid_1's auc: 0.908006 [2700] training's auc: 0.972482 valid_1's auc: 0.908045 [2800] training's auc: 0.973621 valid_1's auc: 0.908107 [2900] training's auc: 0.974728 valid_1's auc: 0.908186 [3000] training's auc: 0.975764 valid_1's auc: 0.90824 [3100] training's auc: 0.976771 valid_1's auc: 0.908233 Early stopping, best iteration is: [3002] training's auc: 0.975789 valid_1's auc: 0.908241 Partial score of fold 4 is: 0.9082407027920657 Our oof AUC score is: 0.9068186729520137 auc: 0.9068186729520137 | 16 | 0.9068 | 0.5407 | 2.788 | 4.56 | 0.005362 | 15.85 | 4.922 | 4.002 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.896821 valid_1's auc: 0.882074 [200] training's auc: 0.908553 valid_1's auc: 0.888484 [300] training's auc: 0.917863 valid_1's auc: 0.893882 [400] training's auc: 0.925307 valid_1's auc: 0.897655 [500] training's auc: 0.931052 valid_1's auc: 0.899842 [600] training's auc: 0.93596 valid_1's auc: 0.901397 [700] training's auc: 0.940116 valid_1's auc: 0.902309 [800] training's auc: 0.944275 valid_1's auc: 0.903219 [900] training's auc: 0.94787 valid_1's auc: 0.903847 [1000] training's auc: 0.951183 valid_1's auc: 0.904359 [1100] training's auc: 0.953994 valid_1's auc: 0.904936 [1200] training's auc: 0.957023 valid_1's auc: 0.905374 [1300] training's auc: 0.959608 valid_1's auc: 0.905702 [1400] training's auc: 0.96202 valid_1's auc: 0.905766 [1500] training's auc: 0.964505 valid_1's auc: 0.906181 [1600] training's auc: 0.966646 valid_1's auc: 0.906664 Early stopping, best iteration is: [1595] training's auc: 0.966539 valid_1's auc: 0.906679 Partial score of fold 0 is: 0.9066791099344617 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.898747 valid_1's auc: 0.879592 [200] training's auc: 0.909223 valid_1's auc: 0.885734 [300] training's auc: 0.918445 valid_1's auc: 0.891158 [400] training's auc: 0.926167 valid_1's auc: 0.895577 [500] training's auc: 0.931949 valid_1's auc: 0.898111 [600] training's auc: 0.936902 valid_1's auc: 0.899633 [700] training's auc: 0.940994 valid_1's auc: 0.900939 [800] training's auc: 0.944826 valid_1's auc: 0.901634 [900] training's auc: 0.948483 valid_1's auc: 0.90226 [1000] training's auc: 0.951818 valid_1's auc: 0.902832 [1100] training's auc: 0.95479 valid_1's auc: 0.903117 [1200] training's auc: 0.957655 valid_1's auc: 0.903501 [1300] training's auc: 0.960165 valid_1's auc: 0.903553 [1400] training's auc: 0.962484 valid_1's auc: 0.90365 [1500] training's auc: 0.964844 valid_1's auc: 0.903774 [1600] training's auc: 0.967003 valid_1's auc: 0.903949 [1700] training's auc: 0.968982 valid_1's auc: 0.904151 [1800] training's auc: 0.970821 valid_1's auc: 0.904225 [1900] training's auc: 0.972419 valid_1's auc: 0.904319 [2000] training's auc: 0.974004 valid_1's auc: 0.904464 [2100] training's auc: 0.975508 valid_1's auc: 0.904478 Early stopping, best iteration is: [2087] training's auc: 0.975331 valid_1's auc: 0.9045 Partial score of fold 1 is: 0.9045001183517595 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.897911 valid_1's auc: 0.87725 [200] training's auc: 0.910329 valid_1's auc: 0.884487 [300] training's auc: 0.918799 valid_1's auc: 0.889449 [400] training's auc: 0.926209 valid_1's auc: 0.893075 [500] training's auc: 0.931878 valid_1's auc: 0.895206 [600] training's auc: 0.936695 valid_1's auc: 0.896344 [700] training's auc: 0.941036 valid_1's auc: 0.897175 [800] training's auc: 0.945032 valid_1's auc: 0.89807 [900] training's auc: 0.948376 valid_1's auc: 0.898594 [1000] training's auc: 0.951767 valid_1's auc: 0.899075 [1100] training's auc: 0.954834 valid_1's auc: 0.899375 [1200] training's auc: 0.957656 valid_1's auc: 0.899707 [1300] training's auc: 0.960191 valid_1's auc: 0.899748 [1400] training's auc: 0.962734 valid_1's auc: 0.900012 [1500] training's auc: 0.964821 valid_1's auc: 0.900164 [1600] training's auc: 0.966976 valid_1's auc: 0.90025 [1700] training's auc: 0.969042 valid_1's auc: 0.900448 [1800] training's auc: 0.970975 valid_1's auc: 0.90051 [1900] training's auc: 0.97259 valid_1's auc: 0.900481 Early stopping, best iteration is: [1857] training's auc: 0.971925 valid_1's auc: 0.900612 Partial score of fold 2 is: 0.9006124515097012 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.897742 valid_1's auc: 0.884525 [200] training's auc: 0.909431 valid_1's auc: 0.88996 [300] training's auc: 0.918904 valid_1's auc: 0.894203 [400] training's auc: 0.926302 valid_1's auc: 0.897309 [500] training's auc: 0.932089 valid_1's auc: 0.89943 [600] training's auc: 0.936753 valid_1's auc: 0.900774 [700] training's auc: 0.940974 valid_1's auc: 0.901735 [800] training's auc: 0.94494 valid_1's auc: 0.902193 [900] training's auc: 0.94858 valid_1's auc: 0.902799 [1000] training's auc: 0.951817 valid_1's auc: 0.903362 [1100] training's auc: 0.954751 valid_1's auc: 0.903587 [1200] training's auc: 0.957757 valid_1's auc: 0.903746 [1300] training's auc: 0.960525 valid_1's auc: 0.903818 [1400] training's auc: 0.962801 valid_1's auc: 0.90383 Early stopping, best iteration is: [1332] training's auc: 0.961196 valid_1's auc: 0.903995 Partial score of fold 3 is: 0.9039949758564966 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.897243 valid_1's auc: 0.880857 [200] training's auc: 0.908782 valid_1's auc: 0.886934 [300] training's auc: 0.918149 valid_1's auc: 0.892552 [400] training's auc: 0.925307 valid_1's auc: 0.896549 [500] training's auc: 0.931264 valid_1's auc: 0.899202 [600] training's auc: 0.936075 valid_1's auc: 0.900545 [700] training's auc: 0.940408 valid_1's auc: 0.901541 [800] training's auc: 0.944303 valid_1's auc: 0.902362 [900] training's auc: 0.947724 valid_1's auc: 0.903073 [1000] training's auc: 0.95119 valid_1's auc: 0.903755 [1100] training's auc: 0.954207 valid_1's auc: 0.904296 [1200] training's auc: 0.957025 valid_1's auc: 0.904871 [1300] training's auc: 0.959626 valid_1's auc: 0.905081 [1400] training's auc: 0.962086 valid_1's auc: 0.905259 Early stopping, best iteration is: [1359] training's auc: 0.961056 valid_1's auc: 0.905285 Partial score of fold 4 is: 0.9052850870108845 Our oof AUC score is: 0.9040954500623035 auc: 0.9040954500623035 | 17 | 0.9041 | 0.9537 | 4.718 | 4.643 | 0.009437 | 16.53 | 1.003 | 9.899 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.902582 valid_1's auc: 0.885262 [200] training's auc: 0.918121 valid_1's auc: 0.893365 [300] training's auc: 0.928334 valid_1's auc: 0.898278 [400] training's auc: 0.93595 valid_1's auc: 0.901148 [500] training's auc: 0.942439 valid_1's auc: 0.902767 [600] training's auc: 0.947923 valid_1's auc: 0.904273 [700] training's auc: 0.953029 valid_1's auc: 0.905016 [800] training's auc: 0.95747 valid_1's auc: 0.905439 [900] training's auc: 0.961444 valid_1's auc: 0.905914 [1000] training's auc: 0.96501 valid_1's auc: 0.906279 [1100] training's auc: 0.968227 valid_1's auc: 0.906557 [1200] training's auc: 0.971367 valid_1's auc: 0.906765 [1300] training's auc: 0.973933 valid_1's auc: 0.906857 [1400] training's auc: 0.976334 valid_1's auc: 0.90682 [1500] training's auc: 0.978708 valid_1's auc: 0.907022 [1600] training's auc: 0.980652 valid_1's auc: 0.907095 [1700] training's auc: 0.982426 valid_1's auc: 0.907061 Early stopping, best iteration is: [1665] training's auc: 0.981797 valid_1's auc: 0.907153 Partial score of fold 0 is: 0.9071530446555511 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.903434 valid_1's auc: 0.883542 [200] training's auc: 0.917434 valid_1's auc: 0.890742 [300] training's auc: 0.928114 valid_1's auc: 0.896009 [400] training's auc: 0.935906 valid_1's auc: 0.899238 [500] training's auc: 0.942207 valid_1's auc: 0.901319 [600] training's auc: 0.947954 valid_1's auc: 0.902299 [700] training's auc: 0.952894 valid_1's auc: 0.903014 [800] training's auc: 0.957452 valid_1's auc: 0.903668 [900] training's auc: 0.96179 valid_1's auc: 0.903836 [1000] training's auc: 0.965454 valid_1's auc: 0.904068 [1100] training's auc: 0.968818 valid_1's auc: 0.904204 [1200] training's auc: 0.971722 valid_1's auc: 0.90432 [1300] training's auc: 0.974281 valid_1's auc: 0.904411 [1400] training's auc: 0.976605 valid_1's auc: 0.904418 Early stopping, best iteration is: [1370] training's auc: 0.975818 valid_1's auc: 0.904523 Partial score of fold 1 is: 0.9045230725624438 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.903434 valid_1's auc: 0.880485 [200] training's auc: 0.918375 valid_1's auc: 0.88848 [300] training's auc: 0.928455 valid_1's auc: 0.89342 [400] training's auc: 0.936243 valid_1's auc: 0.896031 [500] training's auc: 0.942787 valid_1's auc: 0.897675 [600] training's auc: 0.948173 valid_1's auc: 0.898625 [700] training's auc: 0.953205 valid_1's auc: 0.899186 [800] training's auc: 0.957661 valid_1's auc: 0.900022 [900] training's auc: 0.961579 valid_1's auc: 0.900327 [1000] training's auc: 0.965375 valid_1's auc: 0.900553 [1100] training's auc: 0.968744 valid_1's auc: 0.900481 Early stopping, best iteration is: [1028] training's auc: 0.966481 valid_1's auc: 0.900708 Partial score of fold 2 is: 0.900707622908679 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.903735 valid_1's auc: 0.887609 [200] training's auc: 0.918061 valid_1's auc: 0.89374 [300] training's auc: 0.928823 valid_1's auc: 0.897997 [400] training's auc: 0.936467 valid_1's auc: 0.900204 [500] training's auc: 0.942992 valid_1's auc: 0.901683 [600] training's auc: 0.948491 valid_1's auc: 0.902755 [700] training's auc: 0.953492 valid_1's auc: 0.903333 [800] training's auc: 0.958113 valid_1's auc: 0.903788 [900] training's auc: 0.962166 valid_1's auc: 0.903943 Early stopping, best iteration is: [839] training's auc: 0.959621 valid_1's auc: 0.903971 Partial score of fold 3 is: 0.9039705125204597 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.902604 valid_1's auc: 0.883563 [200] training's auc: 0.917194 valid_1's auc: 0.890796 [300] training's auc: 0.92786 valid_1's auc: 0.897038 [400] training's auc: 0.935375 valid_1's auc: 0.900036 [500] training's auc: 0.942076 valid_1's auc: 0.902109 [600] training's auc: 0.947591 valid_1's auc: 0.903099 [700] training's auc: 0.952814 valid_1's auc: 0.904068 [800] training's auc: 0.957556 valid_1's auc: 0.904817 [900] training's auc: 0.961486 valid_1's auc: 0.90512 [1000] training's auc: 0.965139 valid_1's auc: 0.905741 [1100] training's auc: 0.968301 valid_1's auc: 0.905859 [1200] training's auc: 0.971116 valid_1's auc: 0.905982 [1300] training's auc: 0.97368 valid_1's auc: 0.906052 [1400] training's auc: 0.976165 valid_1's auc: 0.906281 Early stopping, best iteration is: [1388] training's auc: 0.975839 valid_1's auc: 0.906301 Partial score of fold 4 is: 0.9063010127934201 Our oof AUC score is: 0.9044652470760501 auc: 0.9044652470760501 | 18 | 0.9045 | 0.944 | 0.1648 | 4.82 | 0.01237 | 15.05 | 1.057 | 9.867 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.906448 valid_1's auc: 0.886464 [200] training's auc: 0.923145 valid_1's auc: 0.895505 [300] training's auc: 0.933518 valid_1's auc: 0.899788 [400] training's auc: 0.941298 valid_1's auc: 0.902417 [500] training's auc: 0.948279 valid_1's auc: 0.903647 [600] training's auc: 0.954057 valid_1's auc: 0.904576 [700] training's auc: 0.959568 valid_1's auc: 0.905105 [800] training's auc: 0.964035 valid_1's auc: 0.90548 [900] training's auc: 0.967814 valid_1's auc: 0.905862 [1000] training's auc: 0.971481 valid_1's auc: 0.906131 [1100] training's auc: 0.97459 valid_1's auc: 0.906307 [1200] training's auc: 0.977681 valid_1's auc: 0.906287 Early stopping, best iteration is: [1129] training's auc: 0.975601 valid_1's auc: 0.906357 Partial score of fold 0 is: 0.9063566956188741 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.90616 valid_1's auc: 0.88428 [200] training's auc: 0.922136 valid_1's auc: 0.892576 [300] training's auc: 0.93347 valid_1's auc: 0.897361 [400] training's auc: 0.94145 valid_1's auc: 0.900255 [500] training's auc: 0.948236 valid_1's auc: 0.901485 [600] training's auc: 0.954227 valid_1's auc: 0.902371 [700] training's auc: 0.959427 valid_1's auc: 0.903125 [800] training's auc: 0.964178 valid_1's auc: 0.903509 [900] training's auc: 0.968409 valid_1's auc: 0.90379 [1000] training's auc: 0.97191 valid_1's auc: 0.903668 Early stopping, best iteration is: [966] training's auc: 0.97082 valid_1's auc: 0.903874 Partial score of fold 1 is: 0.9038743240003798 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.90719 valid_1's auc: 0.882868 [200] training's auc: 0.923336 valid_1's auc: 0.891142 [300] training's auc: 0.933941 valid_1's auc: 0.895568 [400] training's auc: 0.942033 valid_1's auc: 0.897283 [500] training's auc: 0.948895 valid_1's auc: 0.898747 [600] training's auc: 0.954312 valid_1's auc: 0.899181 [700] training's auc: 0.95976 valid_1's auc: 0.899674 [800] training's auc: 0.964449 valid_1's auc: 0.900358 [900] training's auc: 0.968166 valid_1's auc: 0.900643 [1000] training's auc: 0.971859 valid_1's auc: 0.900769 [1100] training's auc: 0.975232 valid_1's auc: 0.900711 Early stopping, best iteration is: [1035] training's auc: 0.973139 valid_1's auc: 0.901014 Partial score of fold 2 is: 0.9010141690424965 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.907648 valid_1's auc: 0.888997 [200] training's auc: 0.92319 valid_1's auc: 0.89586 [300] training's auc: 0.93392 valid_1's auc: 0.89945 [400] training's auc: 0.94217 valid_1's auc: 0.901067 [500] training's auc: 0.948881 valid_1's auc: 0.902202 [600] training's auc: 0.954591 valid_1's auc: 0.903013 [700] training's auc: 0.959883 valid_1's auc: 0.903261 [800] training's auc: 0.964452 valid_1's auc: 0.903588 [900] training's auc: 0.96856 valid_1's auc: 0.903523 Early stopping, best iteration is: [837] training's auc: 0.965926 valid_1's auc: 0.903697 Partial score of fold 3 is: 0.9036967794753137 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.90539 valid_1's auc: 0.88489 [200] training's auc: 0.922125 valid_1's auc: 0.893792 [300] training's auc: 0.933256 valid_1's auc: 0.898771 [400] training's auc: 0.94122 valid_1's auc: 0.901 [500] training's auc: 0.948247 valid_1's auc: 0.902872 [600] training's auc: 0.953883 valid_1's auc: 0.904011 [700] training's auc: 0.959631 valid_1's auc: 0.90471 [800] training's auc: 0.964319 valid_1's auc: 0.905099 [900] training's auc: 0.968407 valid_1's auc: 0.905139 [1000] training's auc: 0.971982 valid_1's auc: 0.905762 Early stopping, best iteration is: [998] training's auc: 0.971911 valid_1's auc: 0.905787 Partial score of fold 4 is: 0.9057865288588018 Our oof AUC score is: 0.9041121222278096 auc: 0.9041121222278096 | 19 | 0.9041 | 0.9797 | 0.3888 | 4.806 | 0.01515 | 13.54 | 1.038 | 9.881 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.898044 valid_1's auc: 0.884509 [200] training's auc: 0.908465 valid_1's auc: 0.890167 [300] training's auc: 0.916766 valid_1's auc: 0.894674 [400] training's auc: 0.923793 valid_1's auc: 0.898201 [500] training's auc: 0.929323 valid_1's auc: 0.900608 [600] training's auc: 0.933965 valid_1's auc: 0.902232 [700] training's auc: 0.938204 valid_1's auc: 0.903406 [800] training's auc: 0.942007 valid_1's auc: 0.904392 [900] training's auc: 0.945541 valid_1's auc: 0.905047 [1000] training's auc: 0.94889 valid_1's auc: 0.905717 [1100] training's auc: 0.952083 valid_1's auc: 0.906238 [1200] training's auc: 0.954966 valid_1's auc: 0.906735 [1300] training's auc: 0.957687 valid_1's auc: 0.907159 [1400] training's auc: 0.960208 valid_1's auc: 0.907462 [1500] training's auc: 0.962645 valid_1's auc: 0.907757 [1600] training's auc: 0.964862 valid_1's auc: 0.908063 [1700] training's auc: 0.967005 valid_1's auc: 0.908355 [1800] training's auc: 0.969017 valid_1's auc: 0.908492 [1900] training's auc: 0.97089 valid_1's auc: 0.908648 [2000] training's auc: 0.972702 valid_1's auc: 0.908762 [2100] training's auc: 0.974327 valid_1's auc: 0.908809 [2200] training's auc: 0.975923 valid_1's auc: 0.908863 [2300] training's auc: 0.977406 valid_1's auc: 0.90902 [2400] training's auc: 0.978841 valid_1's auc: 0.909011 [2500] training's auc: 0.980143 valid_1's auc: 0.909104 [2600] training's auc: 0.981375 valid_1's auc: 0.909002 Early stopping, best iteration is: [2506] training's auc: 0.980213 valid_1's auc: 0.909112 Partial score of fold 0 is: 0.9091122939586326 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.898713 valid_1's auc: 0.88178 [200] training's auc: 0.908821 valid_1's auc: 0.88797 [300] training's auc: 0.917053 valid_1's auc: 0.892579 [400] training's auc: 0.924089 valid_1's auc: 0.896227 [500] training's auc: 0.9297 valid_1's auc: 0.899002 [600] training's auc: 0.93442 valid_1's auc: 0.900701 [700] training's auc: 0.938534 valid_1's auc: 0.901971 [800] training's auc: 0.942305 valid_1's auc: 0.902988 [900] training's auc: 0.945969 valid_1's auc: 0.903782 [1000] training's auc: 0.949271 valid_1's auc: 0.904346 [1100] training's auc: 0.952356 valid_1's auc: 0.904756 [1200] training's auc: 0.955393 valid_1's auc: 0.905241 [1300] training's auc: 0.958118 valid_1's auc: 0.905513 [1400] training's auc: 0.960674 valid_1's auc: 0.905902 [1500] training's auc: 0.963153 valid_1's auc: 0.906265 [1600] training's auc: 0.965324 valid_1's auc: 0.906361 [1700] training's auc: 0.967425 valid_1's auc: 0.906516 [1800] training's auc: 0.969393 valid_1's auc: 0.906663 [1900] training's auc: 0.971286 valid_1's auc: 0.906721 [2000] training's auc: 0.973035 valid_1's auc: 0.906833 [2100] training's auc: 0.974656 valid_1's auc: 0.906829 [2200] training's auc: 0.976212 valid_1's auc: 0.906983 [2300] training's auc: 0.977711 valid_1's auc: 0.907027 [2400] training's auc: 0.979108 valid_1's auc: 0.90705 [2500] training's auc: 0.980441 valid_1's auc: 0.907103 [2600] training's auc: 0.981619 valid_1's auc: 0.907157 [2700] training's auc: 0.982788 valid_1's auc: 0.907111 Early stopping, best iteration is: [2603] training's auc: 0.981649 valid_1's auc: 0.907158 Partial score of fold 1 is: 0.9071577184193029 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.899689 valid_1's auc: 0.879914 [200] training's auc: 0.909613 valid_1's auc: 0.885548 [300] training's auc: 0.917366 valid_1's auc: 0.88975 [400] training's auc: 0.924227 valid_1's auc: 0.893392 [500] training's auc: 0.929831 valid_1's auc: 0.896113 [600] training's auc: 0.934634 valid_1's auc: 0.897755 [700] training's auc: 0.938811 valid_1's auc: 0.898812 [800] training's auc: 0.942514 valid_1's auc: 0.89958 [900] training's auc: 0.946043 valid_1's auc: 0.900227 [1000] training's auc: 0.949275 valid_1's auc: 0.900761 [1100] training's auc: 0.952487 valid_1's auc: 0.901112 [1200] training's auc: 0.955423 valid_1's auc: 0.901348 [1300] training's auc: 0.958237 valid_1's auc: 0.901573 [1400] training's auc: 0.960732 valid_1's auc: 0.901852 [1500] training's auc: 0.96317 valid_1's auc: 0.902021 [1600] training's auc: 0.965422 valid_1's auc: 0.902134 [1700] training's auc: 0.967608 valid_1's auc: 0.902336 [1800] training's auc: 0.969503 valid_1's auc: 0.902507 [1900] training's auc: 0.971367 valid_1's auc: 0.902662 [2000] training's auc: 0.97313 valid_1's auc: 0.902779 Early stopping, best iteration is: [1968] training's auc: 0.972613 valid_1's auc: 0.902804 Partial score of fold 2 is: 0.9028042582510776 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.89803 valid_1's auc: 0.886823 [200] training's auc: 0.908199 valid_1's auc: 0.891599 [300] training's auc: 0.916503 valid_1's auc: 0.895397 [400] training's auc: 0.923719 valid_1's auc: 0.898384 [500] training's auc: 0.929353 valid_1's auc: 0.900259 [600] training's auc: 0.934242 valid_1's auc: 0.901609 [700] training's auc: 0.938386 valid_1's auc: 0.902765 [800] training's auc: 0.942177 valid_1's auc: 0.90354 [900] training's auc: 0.945819 valid_1's auc: 0.904056 [1000] training's auc: 0.949123 valid_1's auc: 0.904532 [1100] training's auc: 0.952416 valid_1's auc: 0.904951 [1200] training's auc: 0.955462 valid_1's auc: 0.905184 [1300] training's auc: 0.958208 valid_1's auc: 0.905394 [1400] training's auc: 0.960755 valid_1's auc: 0.905495 [1500] training's auc: 0.963164 valid_1's auc: 0.905687 [1600] training's auc: 0.965464 valid_1's auc: 0.905776 [1700] training's auc: 0.967583 valid_1's auc: 0.905944 [1800] training's auc: 0.969568 valid_1's auc: 0.906058 [1900] training's auc: 0.97148 valid_1's auc: 0.906202 [2000] training's auc: 0.973188 valid_1's auc: 0.906312 [2100] training's auc: 0.974905 valid_1's auc: 0.906387 [2200] training's auc: 0.976439 valid_1's auc: 0.906524 [2300] training's auc: 0.977843 valid_1's auc: 0.906555 [2400] training's auc: 0.979187 valid_1's auc: 0.906542 Early stopping, best iteration is: [2311] training's auc: 0.977993 valid_1's auc: 0.906573 Partial score of fold 3 is: 0.906573388858447 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.898478 valid_1's auc: 0.882974 [200] training's auc: 0.908317 valid_1's auc: 0.888471 [300] training's auc: 0.916413 valid_1's auc: 0.893061 [400] training's auc: 0.923443 valid_1's auc: 0.897019 [500] training's auc: 0.929168 valid_1's auc: 0.899841 [600] training's auc: 0.933965 valid_1's auc: 0.901589 [700] training's auc: 0.938173 valid_1's auc: 0.902681 [800] training's auc: 0.94204 valid_1's auc: 0.903668 [900] training's auc: 0.945674 valid_1's auc: 0.904484 [1000] training's auc: 0.94904 valid_1's auc: 0.905084 [1100] training's auc: 0.952175 valid_1's auc: 0.905568 [1200] training's auc: 0.955155 valid_1's auc: 0.906138 [1300] training's auc: 0.957939 valid_1's auc: 0.906429 [1400] training's auc: 0.960469 valid_1's auc: 0.906773 [1500] training's auc: 0.962792 valid_1's auc: 0.907185 [1600] training's auc: 0.965202 valid_1's auc: 0.907293 [1700] training's auc: 0.96731 valid_1's auc: 0.907445 [1800] training's auc: 0.969341 valid_1's auc: 0.907643 [1900] training's auc: 0.971219 valid_1's auc: 0.907791 [2000] training's auc: 0.972932 valid_1's auc: 0.907824 [2100] training's auc: 0.974582 valid_1's auc: 0.907903 [2200] training's auc: 0.976126 valid_1's auc: 0.908016 [2300] training's auc: 0.977642 valid_1's auc: 0.908018 [2400] training's auc: 0.978975 valid_1's auc: 0.908123 [2500] training's auc: 0.98029 valid_1's auc: 0.908202 Early stopping, best iteration is: [2490] training's auc: 0.98016 valid_1's auc: 0.908236 Partial score of fold 4 is: 0.9082357271982955 Our oof AUC score is: 0.9067586694393703 auc: 0.9067586694393703 | 20 | 0.9068 | 0.7743 | 0.8008 | 4.974 | 0.008075 | 16.98 | 1.112 | 1.128 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.909006 valid_1's auc: 0.891416 [200] training's auc: 0.922713 valid_1's auc: 0.898592 [300] training's auc: 0.932601 valid_1's auc: 0.902501 [400] training's auc: 0.940376 valid_1's auc: 0.904835 [500] training's auc: 0.947121 valid_1's auc: 0.906158 [600] training's auc: 0.953181 valid_1's auc: 0.907302 [700] training's auc: 0.958555 valid_1's auc: 0.907784 [800] training's auc: 0.963175 valid_1's auc: 0.90807 [900] training's auc: 0.96748 valid_1's auc: 0.908389 [1000] training's auc: 0.971051 valid_1's auc: 0.908474 [1100] training's auc: 0.974251 valid_1's auc: 0.908679 [1200] training's auc: 0.97714 valid_1's auc: 0.908725 Early stopping, best iteration is: [1163] training's auc: 0.976116 valid_1's auc: 0.908801 Partial score of fold 0 is: 0.9088013755942088 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.909217 valid_1's auc: 0.888755 [200] training's auc: 0.92272 valid_1's auc: 0.896414 [300] training's auc: 0.932767 valid_1's auc: 0.900648 [400] training's auc: 0.940562 valid_1's auc: 0.903113 [500] training's auc: 0.947598 valid_1's auc: 0.904412 [600] training's auc: 0.953722 valid_1's auc: 0.905484 [700] training's auc: 0.959031 valid_1's auc: 0.9061 [800] training's auc: 0.963678 valid_1's auc: 0.906555 [900] training's auc: 0.96784 valid_1's auc: 0.906786 [1000] training's auc: 0.971486 valid_1's auc: 0.907022 [1100] training's auc: 0.974792 valid_1's auc: 0.907321 [1200] training's auc: 0.977714 valid_1's auc: 0.907294 Early stopping, best iteration is: [1122] training's auc: 0.97543 valid_1's auc: 0.907406 Partial score of fold 1 is: 0.907406106347725 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.909478 valid_1's auc: 0.886307 [200] training's auc: 0.923581 valid_1's auc: 0.893391 [300] training's auc: 0.933384 valid_1's auc: 0.897576 [400] training's auc: 0.941188 valid_1's auc: 0.899598 [500] training's auc: 0.947949 valid_1's auc: 0.900883 [600] training's auc: 0.954009 valid_1's auc: 0.901714 [700] training's auc: 0.959245 valid_1's auc: 0.902053 [800] training's auc: 0.9638 valid_1's auc: 0.902429 [900] training's auc: 0.967862 valid_1's auc: 0.902798 [1000] training's auc: 0.971362 valid_1's auc: 0.903009 [1100] training's auc: 0.974603 valid_1's auc: 0.903065 [1200] training's auc: 0.977649 valid_1's auc: 0.903238 [1300] training's auc: 0.980266 valid_1's auc: 0.903171 Early stopping, best iteration is: [1274] training's auc: 0.979602 valid_1's auc: 0.90334 Partial score of fold 2 is: 0.9033396680421605 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.909413 valid_1's auc: 0.8917 [200] training's auc: 0.923134 valid_1's auc: 0.898187 [300] training's auc: 0.933117 valid_1's auc: 0.90176 [400] training's auc: 0.941075 valid_1's auc: 0.903569 [500] training's auc: 0.947696 valid_1's auc: 0.904794 [600] training's auc: 0.95394 valid_1's auc: 0.905547 [700] training's auc: 0.959209 valid_1's auc: 0.906086 [800] training's auc: 0.963998 valid_1's auc: 0.906602 [900] training's auc: 0.968265 valid_1's auc: 0.906995 [1000] training's auc: 0.971692 valid_1's auc: 0.907059 [1100] training's auc: 0.974969 valid_1's auc: 0.907099 [1200] training's auc: 0.977856 valid_1's auc: 0.907119 [1300] training's auc: 0.980397 valid_1's auc: 0.907242 [1400] training's auc: 0.982738 valid_1's auc: 0.907166 Early stopping, best iteration is: [1301] training's auc: 0.980419 valid_1's auc: 0.907262 Partial score of fold 3 is: 0.9072618677994536 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.908932 valid_1's auc: 0.890047 [200] training's auc: 0.922511 valid_1's auc: 0.897589 [300] training's auc: 0.932516 valid_1's auc: 0.90208 [400] training's auc: 0.940385 valid_1's auc: 0.90432 [500] training's auc: 0.947212 valid_1's auc: 0.905792 [600] training's auc: 0.953521 valid_1's auc: 0.906744 [700] training's auc: 0.958798 valid_1's auc: 0.907353 [800] training's auc: 0.963263 valid_1's auc: 0.907807 [900] training's auc: 0.967532 valid_1's auc: 0.907922 [1000] training's auc: 0.971149 valid_1's auc: 0.908282 [1100] training's auc: 0.974469 valid_1's auc: 0.908546 [1200] training's auc: 0.977404 valid_1's auc: 0.908812 [1300] training's auc: 0.980014 valid_1's auc: 0.908731 Early stopping, best iteration is: [1218] training's auc: 0.977901 valid_1's auc: 0.908868 Partial score of fold 4 is: 0.9088684945666329 Our oof AUC score is: 0.9070693178073428 auc: 0.9070693178073428 | 21 | 0.9071 | 0.6774 | 0.02145 | 4.984 | 0.01536 | 16.94 | 1.246 | 1.34 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.904118 valid_1's auc: 0.889589 [200] training's auc: 0.916242 valid_1's auc: 0.896039 [300] training's auc: 0.925455 valid_1's auc: 0.900812 [400] training's auc: 0.932609 valid_1's auc: 0.903701 [500] training's auc: 0.938686 valid_1's auc: 0.905264 [600] training's auc: 0.944157 valid_1's auc: 0.906543 [700] training's auc: 0.949018 valid_1's auc: 0.907535 [800] training's auc: 0.953357 valid_1's auc: 0.908213 [900] training's auc: 0.957564 valid_1's auc: 0.908497 Early stopping, best iteration is: [883] training's auc: 0.956952 valid_1's auc: 0.908535 Partial score of fold 0 is: 0.9085348956770701 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.904615 valid_1's auc: 0.887826 [200] training's auc: 0.91649 valid_1's auc: 0.894051 [300] training's auc: 0.925866 valid_1's auc: 0.89888 [400] training's auc: 0.933084 valid_1's auc: 0.901573 [500] training's auc: 0.939162 valid_1's auc: 0.903391 [600] training's auc: 0.944648 valid_1's auc: 0.904527 [700] training's auc: 0.949518 valid_1's auc: 0.905312 [800] training's auc: 0.953883 valid_1's auc: 0.905926 [900] training's auc: 0.958027 valid_1's auc: 0.906465 [1000] training's auc: 0.961784 valid_1's auc: 0.906937 [1100] training's auc: 0.965182 valid_1's auc: 0.907168 [1200] training's auc: 0.96822 valid_1's auc: 0.907392 [1300] training's auc: 0.970984 valid_1's auc: 0.907509 [1400] training's auc: 0.973641 valid_1's auc: 0.907622 [1500] training's auc: 0.976032 valid_1's auc: 0.907702 Early stopping, best iteration is: [1466] training's auc: 0.975264 valid_1's auc: 0.907716 Partial score of fold 1 is: 0.9077163839542153 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.905612 valid_1's auc: 0.885085 [200] training's auc: 0.916921 valid_1's auc: 0.891274 [300] training's auc: 0.926082 valid_1's auc: 0.895897 [400] training's auc: 0.933256 valid_1's auc: 0.898529 [500] training's auc: 0.939419 valid_1's auc: 0.900124 [600] training's auc: 0.944975 valid_1's auc: 0.900999 [700] training's auc: 0.94982 valid_1's auc: 0.901722 [800] training's auc: 0.954102 valid_1's auc: 0.902248 [900] training's auc: 0.958112 valid_1's auc: 0.902728 [1000] training's auc: 0.96168 valid_1's auc: 0.903206 [1100] training's auc: 0.965153 valid_1's auc: 0.90343 [1200] training's auc: 0.968308 valid_1's auc: 0.903655 [1300] training's auc: 0.971163 valid_1's auc: 0.903786 [1400] training's auc: 0.973648 valid_1's auc: 0.904007 [1500] training's auc: 0.976081 valid_1's auc: 0.904146 [1600] training's auc: 0.978219 valid_1's auc: 0.904347 [1700] training's auc: 0.980273 valid_1's auc: 0.90442 [1800] training's auc: 0.98213 valid_1's auc: 0.904458 [1900] training's auc: 0.983743 valid_1's auc: 0.904619 [2000] training's auc: 0.985336 valid_1's auc: 0.904568 Early stopping, best iteration is: [1927] training's auc: 0.984187 valid_1's auc: 0.904628 Partial score of fold 2 is: 0.9046283830134316 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.904876 valid_1's auc: 0.890882 [200] training's auc: 0.916394 valid_1's auc: 0.896613 [300] training's auc: 0.925952 valid_1's auc: 0.900577 [400] training's auc: 0.933278 valid_1's auc: 0.903102 [500] training's auc: 0.939244 valid_1's auc: 0.904334 [600] training's auc: 0.944756 valid_1's auc: 0.905297 [700] training's auc: 0.949626 valid_1's auc: 0.906067 [800] training's auc: 0.954111 valid_1's auc: 0.906472 [900] training's auc: 0.958309 valid_1's auc: 0.90681 [1000] training's auc: 0.961902 valid_1's auc: 0.90703 [1100] training's auc: 0.965182 valid_1's auc: 0.907267 [1200] training's auc: 0.968365 valid_1's auc: 0.907255 [1300] training's auc: 0.971217 valid_1's auc: 0.90743 [1400] training's auc: 0.97396 valid_1's auc: 0.907516 Early stopping, best iteration is: [1354] training's auc: 0.972755 valid_1's auc: 0.907557 Partial score of fold 3 is: 0.9075568978936916 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.904494 valid_1's auc: 0.889127 [200] training's auc: 0.915953 valid_1's auc: 0.895406 [300] training's auc: 0.925297 valid_1's auc: 0.900556 [400] training's auc: 0.932654 valid_1's auc: 0.903255 [500] training's auc: 0.938792 valid_1's auc: 0.905073 [600] training's auc: 0.944236 valid_1's auc: 0.906523 [700] training's auc: 0.949195 valid_1's auc: 0.907354 [800] training's auc: 0.953571 valid_1's auc: 0.907979 [900] training's auc: 0.957809 valid_1's auc: 0.908546 [1000] training's auc: 0.961577 valid_1's auc: 0.90896 [1100] training's auc: 0.964969 valid_1's auc: 0.908969 Early stopping, best iteration is: [1009] training's auc: 0.961846 valid_1's auc: 0.908992 Partial score of fold 4 is: 0.9089922059208284 Our oof AUC score is: 0.9072729932313089 auc: 0.9072729932313089 | 22 | 0.9073 | 0.5349 | 0.000937 | 4.744 | 0.01289 | 15.78 | 1.033 | 1.074 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.900384 valid_1's auc: 0.886926 [200] training's auc: 0.911412 valid_1's auc: 0.893156 [300] training's auc: 0.919688 valid_1's auc: 0.897886 [400] training's auc: 0.926297 valid_1's auc: 0.900911 [500] training's auc: 0.931785 valid_1's auc: 0.903018 [600] training's auc: 0.936519 valid_1's auc: 0.904555 [700] training's auc: 0.940962 valid_1's auc: 0.905724 [800] training's auc: 0.94499 valid_1's auc: 0.906593 [900] training's auc: 0.948627 valid_1's auc: 0.907074 [1000] training's auc: 0.951923 valid_1's auc: 0.907522 [1100] training's auc: 0.955101 valid_1's auc: 0.90791 [1200] training's auc: 0.95789 valid_1's auc: 0.908255 [1300] training's auc: 0.960529 valid_1's auc: 0.908532 [1400] training's auc: 0.963035 valid_1's auc: 0.908878 [1500] training's auc: 0.965388 valid_1's auc: 0.909148 [1600] training's auc: 0.967568 valid_1's auc: 0.909199 [1700] training's auc: 0.969739 valid_1's auc: 0.909308 [1800] training's auc: 0.971697 valid_1's auc: 0.909374 [1900] training's auc: 0.973498 valid_1's auc: 0.909441 [2000] training's auc: 0.975302 valid_1's auc: 0.909496 [2100] training's auc: 0.976844 valid_1's auc: 0.909465 [2200] training's auc: 0.978302 valid_1's auc: 0.909663 [2300] training's auc: 0.979718 valid_1's auc: 0.909665 Early stopping, best iteration is: [2267] training's auc: 0.979271 valid_1's auc: 0.909712 Partial score of fold 0 is: 0.9097118926180163 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.900842 valid_1's auc: 0.883743 [200] training's auc: 0.911644 valid_1's auc: 0.890118 [300] training's auc: 0.920342 valid_1's auc: 0.895378 [400] training's auc: 0.927078 valid_1's auc: 0.898727 [500] training's auc: 0.932615 valid_1's auc: 0.90098 [600] training's auc: 0.937232 valid_1's auc: 0.902495 [700] training's auc: 0.941537 valid_1's auc: 0.903559 [800] training's auc: 0.945567 valid_1's auc: 0.904279 [900] training's auc: 0.949428 valid_1's auc: 0.904995 [1000] training's auc: 0.952748 valid_1's auc: 0.905574 [1100] training's auc: 0.955757 valid_1's auc: 0.905966 [1200] training's auc: 0.958675 valid_1's auc: 0.90626 [1300] training's auc: 0.961337 valid_1's auc: 0.906502 [1400] training's auc: 0.963869 valid_1's auc: 0.906693 [1500] training's auc: 0.966213 valid_1's auc: 0.906837 [1600] training's auc: 0.968379 valid_1's auc: 0.906802 [1700] training's auc: 0.970503 valid_1's auc: 0.906926 [1800] training's auc: 0.972421 valid_1's auc: 0.906947 [1900] training's auc: 0.974232 valid_1's auc: 0.907074 [2000] training's auc: 0.975949 valid_1's auc: 0.906999 Early stopping, best iteration is: [1921] training's auc: 0.974621 valid_1's auc: 0.907093 Partial score of fold 1 is: 0.9070934918593588 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.901868 valid_1's auc: 0.882791 [200] training's auc: 0.912267 valid_1's auc: 0.888304 [300] training's auc: 0.920514 valid_1's auc: 0.893181 [400] training's auc: 0.927213 valid_1's auc: 0.896617 [500] training's auc: 0.932709 valid_1's auc: 0.898582 [600] training's auc: 0.937416 valid_1's auc: 0.899681 [700] training's auc: 0.941634 valid_1's auc: 0.900503 [800] training's auc: 0.945609 valid_1's auc: 0.901255 [900] training's auc: 0.949315 valid_1's auc: 0.901745 [1000] training's auc: 0.952553 valid_1's auc: 0.902157 [1100] training's auc: 0.955804 valid_1's auc: 0.902616 [1200] training's auc: 0.958724 valid_1's auc: 0.902811 [1300] training's auc: 0.961375 valid_1's auc: 0.90298 [1400] training's auc: 0.963798 valid_1's auc: 0.903185 [1500] training's auc: 0.966194 valid_1's auc: 0.903442 [1600] training's auc: 0.968384 valid_1's auc: 0.90363 Early stopping, best iteration is: [1591] training's auc: 0.968191 valid_1's auc: 0.903657 Partial score of fold 2 is: 0.9036565039945603 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.90097 valid_1's auc: 0.888864 [200] training's auc: 0.911739 valid_1's auc: 0.894544 [300] training's auc: 0.920598 valid_1's auc: 0.898848 [400] training's auc: 0.927345 valid_1's auc: 0.901266 [500] training's auc: 0.932741 valid_1's auc: 0.90289 [600] training's auc: 0.937496 valid_1's auc: 0.903961 [700] training's auc: 0.941812 valid_1's auc: 0.904851 [800] training's auc: 0.945869 valid_1's auc: 0.905666 [900] training's auc: 0.94962 valid_1's auc: 0.906038 [1000] training's auc: 0.952911 valid_1's auc: 0.906424 [1100] training's auc: 0.95598 valid_1's auc: 0.906525 [1200] training's auc: 0.958895 valid_1's auc: 0.906652 [1300] training's auc: 0.961582 valid_1's auc: 0.906788 [1400] training's auc: 0.964116 valid_1's auc: 0.906869 [1500] training's auc: 0.966403 valid_1's auc: 0.90692 [1600] training's auc: 0.968544 valid_1's auc: 0.906965 Early stopping, best iteration is: [1559] training's auc: 0.967676 valid_1's auc: 0.907051 Partial score of fold 3 is: 0.9070508950848182 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.901057 valid_1's auc: 0.885895 [200] training's auc: 0.911302 valid_1's auc: 0.891807 [300] training's auc: 0.920005 valid_1's auc: 0.897141 [400] training's auc: 0.926752 valid_1's auc: 0.900837 [500] training's auc: 0.932276 valid_1's auc: 0.902904 [600] training's auc: 0.937078 valid_1's auc: 0.904277 [700] training's auc: 0.94153 valid_1's auc: 0.905141 [800] training's auc: 0.94548 valid_1's auc: 0.905882 [900] training's auc: 0.949127 valid_1's auc: 0.906369 [1000] training's auc: 0.952594 valid_1's auc: 0.906868 [1100] training's auc: 0.955683 valid_1's auc: 0.907271 [1200] training's auc: 0.958546 valid_1's auc: 0.907649 [1300] training's auc: 0.961273 valid_1's auc: 0.907826 [1400] training's auc: 0.963742 valid_1's auc: 0.908108 [1500] training's auc: 0.966123 valid_1's auc: 0.9083 [1600] training's auc: 0.968386 valid_1's auc: 0.908514 [1700] training's auc: 0.970366 valid_1's auc: 0.908591 Early stopping, best iteration is: [1694] training's auc: 0.97026 valid_1's auc: 0.908616 Partial score of fold 4 is: 0.9086156816316571 Our oof AUC score is: 0.9071923146051201 auc: 0.9071923146051201 | 23 | 0.9072 | 0.6347 | 4.852 | 4.974 | 0.01105 | 16.46 | 1.033 | 1.584 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.907658 valid_1's auc: 0.890755 [200] training's auc: 0.921588 valid_1's auc: 0.898835 [300] training's auc: 0.93128 valid_1's auc: 0.902778 [400] training's auc: 0.938797 valid_1's auc: 0.904983 [500] training's auc: 0.945603 valid_1's auc: 0.906377 [600] training's auc: 0.951234 valid_1's auc: 0.907292 [700] training's auc: 0.956391 valid_1's auc: 0.907766 [800] training's auc: 0.960759 valid_1's auc: 0.908187 [900] training's auc: 0.964757 valid_1's auc: 0.908638 [1000] training's auc: 0.96829 valid_1's auc: 0.908906 [1100] training's auc: 0.971422 valid_1's auc: 0.909069 [1200] training's auc: 0.974352 valid_1's auc: 0.909196 [1300] training's auc: 0.976959 valid_1's auc: 0.909117 Early stopping, best iteration is: [1214] training's auc: 0.974746 valid_1's auc: 0.909251 Partial score of fold 0 is: 0.9092513007386055 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.908269 valid_1's auc: 0.888073 [200] training's auc: 0.922177 valid_1's auc: 0.895876 [300] training's auc: 0.931902 valid_1's auc: 0.90014 [400] training's auc: 0.939497 valid_1's auc: 0.902304 [500] training's auc: 0.945977 valid_1's auc: 0.903725 [600] training's auc: 0.951895 valid_1's auc: 0.904658 [700] training's auc: 0.95684 valid_1's auc: 0.905256 [800] training's auc: 0.96138 valid_1's auc: 0.905748 [900] training's auc: 0.96548 valid_1's auc: 0.905994 [1000] training's auc: 0.968935 valid_1's auc: 0.906283 [1100] training's auc: 0.971985 valid_1's auc: 0.906348 [1200] training's auc: 0.974889 valid_1's auc: 0.906374 Early stopping, best iteration is: [1171] training's auc: 0.974075 valid_1's auc: 0.906507 Partial score of fold 1 is: 0.9065073491165834 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.908537 valid_1's auc: 0.885837 [200] training's auc: 0.922798 valid_1's auc: 0.893933 [300] training's auc: 0.932722 valid_1's auc: 0.897698 [400] training's auc: 0.940185 valid_1's auc: 0.899604 [500] training's auc: 0.9468 valid_1's auc: 0.900921 [600] training's auc: 0.952464 valid_1's auc: 0.90141 [700] training's auc: 0.957417 valid_1's auc: 0.901944 [800] training's auc: 0.961838 valid_1's auc: 0.902337 [900] training's auc: 0.965744 valid_1's auc: 0.90246 [1000] training's auc: 0.96925 valid_1's auc: 0.902729 [1100] training's auc: 0.972317 valid_1's auc: 0.902732 [1200] training's auc: 0.975246 valid_1's auc: 0.902867 [1300] training's auc: 0.977792 valid_1's auc: 0.902879 Early stopping, best iteration is: [1276] training's auc: 0.977214 valid_1's auc: 0.902974 Partial score of fold 2 is: 0.9029737575703666 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.90842 valid_1's auc: 0.891367 [200] training's auc: 0.922434 valid_1's auc: 0.898461 [300] training's auc: 0.932154 valid_1's auc: 0.901821 [400] training's auc: 0.939803 valid_1's auc: 0.903552 [500] training's auc: 0.946274 valid_1's auc: 0.904684 [600] training's auc: 0.952112 valid_1's auc: 0.905302 [700] training's auc: 0.957075 valid_1's auc: 0.905544 [800] training's auc: 0.96149 valid_1's auc: 0.905973 [900] training's auc: 0.965506 valid_1's auc: 0.905938 Early stopping, best iteration is: [841] training's auc: 0.963155 valid_1's auc: 0.906045 Partial score of fold 3 is: 0.9060449581837153 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.907826 valid_1's auc: 0.889883 [200] training's auc: 0.92163 valid_1's auc: 0.898166 [300] training's auc: 0.931782 valid_1's auc: 0.902257 [400] training's auc: 0.939354 valid_1's auc: 0.90418 [500] training's auc: 0.945936 valid_1's auc: 0.905563 [600] training's auc: 0.951944 valid_1's auc: 0.906596 [700] training's auc: 0.956962 valid_1's auc: 0.907069 [800] training's auc: 0.961353 valid_1's auc: 0.907492 [900] training's auc: 0.965291 valid_1's auc: 0.907663 [1000] training's auc: 0.9688 valid_1's auc: 0.907883 [1100] training's auc: 0.972066 valid_1's auc: 0.908053 [1200] training's auc: 0.974825 valid_1's auc: 0.908133 Early stopping, best iteration is: [1195] training's auc: 0.974702 valid_1's auc: 0.908163 Partial score of fold 4 is: 0.9081630533741367 Our oof AUC score is: 0.9065272899964882 auc: 0.9065272899964882 | 24 | 0.9065 | 0.6786 | 4.858 | 4.331 | 0.0178 | 16.3 | 1.002 | 1.337 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.911504 valid_1's auc: 0.891934 [200] training's auc: 0.927062 valid_1's auc: 0.900349 [300] training's auc: 0.937488 valid_1's auc: 0.903746 [400] training's auc: 0.946002 valid_1's auc: 0.905751 [500] training's auc: 0.953366 valid_1's auc: 0.90708 [600] training's auc: 0.959603 valid_1's auc: 0.908038 [700] training's auc: 0.96512 valid_1's auc: 0.908465 [800] training's auc: 0.969897 valid_1's auc: 0.908762 [900] training's auc: 0.974001 valid_1's auc: 0.908934 [1000] training's auc: 0.977379 valid_1's auc: 0.909043 [1100] training's auc: 0.980496 valid_1's auc: 0.909043 Early stopping, best iteration is: [1048] training's auc: 0.978959 valid_1's auc: 0.90911 Partial score of fold 0 is: 0.9091095424686821 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.91187 valid_1's auc: 0.889823 [200] training's auc: 0.927302 valid_1's auc: 0.897802 [300] training's auc: 0.938004 valid_1's auc: 0.901535 [400] training's auc: 0.946579 valid_1's auc: 0.903189 [500] training's auc: 0.954111 valid_1's auc: 0.904489 [600] training's auc: 0.960464 valid_1's auc: 0.905338 [700] training's auc: 0.965707 valid_1's auc: 0.905778 [800] training's auc: 0.970389 valid_1's auc: 0.905821 [900] training's auc: 0.974549 valid_1's auc: 0.905866 Early stopping, best iteration is: [839] training's auc: 0.972185 valid_1's auc: 0.906003 Partial score of fold 1 is: 0.9060033741558958 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.911954 valid_1's auc: 0.887559 [200] training's auc: 0.92738 valid_1's auc: 0.895676 [300] training's auc: 0.938125 valid_1's auc: 0.898984 [400] training's auc: 0.946644 valid_1's auc: 0.900679 [500] training's auc: 0.954061 valid_1's auc: 0.901682 [600] training's auc: 0.960357 valid_1's auc: 0.902364 [700] training's auc: 0.965645 valid_1's auc: 0.902659 [800] training's auc: 0.970366 valid_1's auc: 0.90283 [900] training's auc: 0.974374 valid_1's auc: 0.902887 [1000] training's auc: 0.977768 valid_1's auc: 0.902974 [1100] training's auc: 0.98079 valid_1's auc: 0.903284 [1200] training's auc: 0.983609 valid_1's auc: 0.903272 Early stopping, best iteration is: [1123] training's auc: 0.981453 valid_1's auc: 0.903352 Partial score of fold 2 is: 0.9033515409097558 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.911878 valid_1's auc: 0.893105 [200] training's auc: 0.927489 valid_1's auc: 0.899985 [300] training's auc: 0.93811 valid_1's auc: 0.902878 [400] training's auc: 0.946648 valid_1's auc: 0.904508 [500] training's auc: 0.953929 valid_1's auc: 0.905445 [600] training's auc: 0.960329 valid_1's auc: 0.905893 [700] training's auc: 0.965659 valid_1's auc: 0.906364 [800] training's auc: 0.970316 valid_1's auc: 0.90679 [900] training's auc: 0.974427 valid_1's auc: 0.907077 [1000] training's auc: 0.977947 valid_1's auc: 0.907178 Early stopping, best iteration is: [927] training's auc: 0.975417 valid_1's auc: 0.907285 Partial score of fold 3 is: 0.9072848610736947 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.911391 valid_1's auc: 0.890379 [200] training's auc: 0.926736 valid_1's auc: 0.899089 [300] training's auc: 0.937571 valid_1's auc: 0.90309 [400] training's auc: 0.946036 valid_1's auc: 0.904917 [500] training's auc: 0.953501 valid_1's auc: 0.906221 [600] training's auc: 0.959928 valid_1's auc: 0.907057 [700] training's auc: 0.965328 valid_1's auc: 0.907238 [800] training's auc: 0.969884 valid_1's auc: 0.907524 [900] training's auc: 0.974002 valid_1's auc: 0.907856 [1000] training's auc: 0.977424 valid_1's auc: 0.908011 [1100] training's auc: 0.980586 valid_1's auc: 0.908145 [1200] training's auc: 0.983449 valid_1's auc: 0.908258 [1300] training's auc: 0.985859 valid_1's auc: 0.908224 Early stopping, best iteration is: [1210] training's auc: 0.983715 valid_1's auc: 0.908322 Partial score of fold 4 is: 0.9083223477625679 Our oof AUC score is: 0.9067660511469412 auc: 0.9067660511469412 | 25 | 0.9068 | 0.7097 | 0.09637 | 4.677 | 0.01887 | 16.92 | 1.107 | 1.024 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.899358 valid_1's auc: 0.886976 [200] training's auc: 0.907669 valid_1's auc: 0.891791 [300] training's auc: 0.914542 valid_1's auc: 0.895347 [400] training's auc: 0.920361 valid_1's auc: 0.898506 [500] training's auc: 0.92523 valid_1's auc: 0.900896 [600] training's auc: 0.929528 valid_1's auc: 0.902815 [700] training's auc: 0.933281 valid_1's auc: 0.904161 [800] training's auc: 0.936711 valid_1's auc: 0.905167 [900] training's auc: 0.939914 valid_1's auc: 0.905877 [1000] training's auc: 0.942914 valid_1's auc: 0.906541 [1100] training's auc: 0.945717 valid_1's auc: 0.906978 [1200] training's auc: 0.948436 valid_1's auc: 0.907454 [1300] training's auc: 0.950997 valid_1's auc: 0.907894 [1400] training's auc: 0.953357 valid_1's auc: 0.90822 [1500] training's auc: 0.955656 valid_1's auc: 0.90851 [1600] training's auc: 0.957823 valid_1's auc: 0.90876 [1700] training's auc: 0.959913 valid_1's auc: 0.908919 [1800] training's auc: 0.961929 valid_1's auc: 0.9091 [1900] training's auc: 0.963788 valid_1's auc: 0.909298 [2000] training's auc: 0.965544 valid_1's auc: 0.909451 [2100] training's auc: 0.967185 valid_1's auc: 0.909556 [2200] training's auc: 0.968755 valid_1's auc: 0.909665 [2300] training's auc: 0.970308 valid_1's auc: 0.909798 [2400] training's auc: 0.971786 valid_1's auc: 0.909794 [2500] training's auc: 0.973125 valid_1's auc: 0.909857 [2600] training's auc: 0.97446 valid_1's auc: 0.909923 [2700] training's auc: 0.975661 valid_1's auc: 0.909942 Early stopping, best iteration is: [2643] training's auc: 0.974999 valid_1's auc: 0.909959 Partial score of fold 0 is: 0.9099590744138573 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.899598 valid_1's auc: 0.885021 [200] training's auc: 0.907759 valid_1's auc: 0.889878 [300] training's auc: 0.91485 valid_1's auc: 0.893416 [400] training's auc: 0.920711 valid_1's auc: 0.896422 [500] training's auc: 0.925693 valid_1's auc: 0.898995 [600] training's auc: 0.929923 valid_1's auc: 0.900811 [700] training's auc: 0.933601 valid_1's auc: 0.902158 [800] training's auc: 0.936951 valid_1's auc: 0.903225 [900] training's auc: 0.940162 valid_1's auc: 0.904073 [1000] training's auc: 0.943143 valid_1's auc: 0.90483 [1100] training's auc: 0.945996 valid_1's auc: 0.905314 [1200] training's auc: 0.948736 valid_1's auc: 0.905832 [1300] training's auc: 0.951279 valid_1's auc: 0.906225 [1400] training's auc: 0.953673 valid_1's auc: 0.906559 [1500] training's auc: 0.955977 valid_1's auc: 0.906753 [1600] training's auc: 0.958108 valid_1's auc: 0.906796 [1700] training's auc: 0.960178 valid_1's auc: 0.906925 [1800] training's auc: 0.962136 valid_1's auc: 0.907044 [1900] training's auc: 0.964001 valid_1's auc: 0.907168 [2000] training's auc: 0.965698 valid_1's auc: 0.907241 [2100] training's auc: 0.967362 valid_1's auc: 0.907282 Early stopping, best iteration is: [2032] training's auc: 0.966251 valid_1's auc: 0.907315 Partial score of fold 1 is: 0.9073147418047061 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.900782 valid_1's auc: 0.882618 [200] training's auc: 0.908633 valid_1's auc: 0.887012 [300] training's auc: 0.915261 valid_1's auc: 0.890628 [400] training's auc: 0.92104 valid_1's auc: 0.893789 [500] training's auc: 0.925891 valid_1's auc: 0.896184 [600] training's auc: 0.930131 valid_1's auc: 0.897768 [700] training's auc: 0.933908 valid_1's auc: 0.898875 [800] training's auc: 0.937272 valid_1's auc: 0.899895 [900] training's auc: 0.94044 valid_1's auc: 0.900628 [1000] training's auc: 0.943377 valid_1's auc: 0.901196 [1100] training's auc: 0.946279 valid_1's auc: 0.901509 [1200] training's auc: 0.948943 valid_1's auc: 0.901901 [1300] training's auc: 0.951434 valid_1's auc: 0.902266 [1400] training's auc: 0.953821 valid_1's auc: 0.902558 [1500] training's auc: 0.95614 valid_1's auc: 0.902864 [1600] training's auc: 0.958298 valid_1's auc: 0.903075 [1700] training's auc: 0.960317 valid_1's auc: 0.903315 [1800] training's auc: 0.962192 valid_1's auc: 0.903397 [1900] training's auc: 0.964027 valid_1's auc: 0.903598 [2000] training's auc: 0.965784 valid_1's auc: 0.903705 [2100] training's auc: 0.967416 valid_1's auc: 0.903731 [2200] training's auc: 0.969026 valid_1's auc: 0.903885 [2300] training's auc: 0.970572 valid_1's auc: 0.903897 [2400] training's auc: 0.971953 valid_1's auc: 0.904012 [2500] training's auc: 0.973276 valid_1's auc: 0.904062 [2600] training's auc: 0.974591 valid_1's auc: 0.90408 [2700] training's auc: 0.975829 valid_1's auc: 0.904138 [2800] training's auc: 0.977045 valid_1's auc: 0.904177 [2900] training's auc: 0.978182 valid_1's auc: 0.904241 [3000] training's auc: 0.979279 valid_1's auc: 0.904186 Early stopping, best iteration is: [2915] training's auc: 0.978346 valid_1's auc: 0.904257 Partial score of fold 2 is: 0.9042566303369483 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.899869 valid_1's auc: 0.888264 [200] training's auc: 0.907982 valid_1's auc: 0.892879 [300] training's auc: 0.914798 valid_1's auc: 0.896127 [400] training's auc: 0.920801 valid_1's auc: 0.898862 [500] training's auc: 0.925628 valid_1's auc: 0.90076 [600] training's auc: 0.929934 valid_1's auc: 0.902154 [700] training's auc: 0.933667 valid_1's auc: 0.903222 [800] training's auc: 0.937023 valid_1's auc: 0.904127 [900] training's auc: 0.940284 valid_1's auc: 0.904678 [1000] training's auc: 0.943252 valid_1's auc: 0.90518 [1100] training's auc: 0.946159 valid_1's auc: 0.905606 [1200] training's auc: 0.948909 valid_1's auc: 0.905944 [1300] training's auc: 0.951474 valid_1's auc: 0.906274 [1400] training's auc: 0.953954 valid_1's auc: 0.90652 [1500] training's auc: 0.956242 valid_1's auc: 0.906657 [1600] training's auc: 0.958421 valid_1's auc: 0.906822 [1700] training's auc: 0.960432 valid_1's auc: 0.907026 [1800] training's auc: 0.962328 valid_1's auc: 0.907088 [1900] training's auc: 0.964192 valid_1's auc: 0.907162 [2000] training's auc: 0.965914 valid_1's auc: 0.907201 Early stopping, best iteration is: [1967] training's auc: 0.965354 valid_1's auc: 0.907245 Partial score of fold 3 is: 0.907244867854072 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.899802 valid_1's auc: 0.885389 [200] training's auc: 0.907617 valid_1's auc: 0.890191 [300] training's auc: 0.91444 valid_1's auc: 0.893913 [400] training's auc: 0.920195 valid_1's auc: 0.897459 [500] training's auc: 0.925221 valid_1's auc: 0.900295 [600] training's auc: 0.929517 valid_1's auc: 0.902334 [700] training's auc: 0.933258 valid_1's auc: 0.903623 [800] training's auc: 0.936676 valid_1's auc: 0.904537 [900] training's auc: 0.939914 valid_1's auc: 0.905354 [1000] training's auc: 0.942962 valid_1's auc: 0.906036 [1100] training's auc: 0.945841 valid_1's auc: 0.906568 [1200] training's auc: 0.948608 valid_1's auc: 0.907135 [1300] training's auc: 0.951153 valid_1's auc: 0.907547 [1400] training's auc: 0.953636 valid_1's auc: 0.907967 [1500] training's auc: 0.955902 valid_1's auc: 0.908143 [1600] training's auc: 0.958035 valid_1's auc: 0.908402 [1700] training's auc: 0.960098 valid_1's auc: 0.908647 [1800] training's auc: 0.962062 valid_1's auc: 0.908781 [1900] training's auc: 0.963887 valid_1's auc: 0.908894 [2000] training's auc: 0.965633 valid_1's auc: 0.908983 [2100] training's auc: 0.967299 valid_1's auc: 0.90909 [2200] training's auc: 0.968846 valid_1's auc: 0.909291 [2300] training's auc: 0.970359 valid_1's auc: 0.909271 Early stopping, best iteration is: [2237] training's auc: 0.969396 valid_1's auc: 0.909322 Partial score of fold 4 is: 0.9093215751508595 Our oof AUC score is: 0.9075241899054636 auc: 0.9075241899054636 | 26 | 0.9075 | 0.5242 | 4.172 | 4.644 | 0.007898 | 16.62 | 1.22 | 1.028 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.908407 valid_1's auc: 0.890321 [200] training's auc: 0.923382 valid_1's auc: 0.898823 [300] training's auc: 0.932987 valid_1's auc: 0.902705 [400] training's auc: 0.940741 valid_1's auc: 0.904898 [500] training's auc: 0.947469 valid_1's auc: 0.906222 [600] training's auc: 0.953194 valid_1's auc: 0.907216 [700] training's auc: 0.958393 valid_1's auc: 0.907814 [800] training's auc: 0.962843 valid_1's auc: 0.908129 [900] training's auc: 0.966857 valid_1's auc: 0.908471 [1000] training's auc: 0.970367 valid_1's auc: 0.908681 [1100] training's auc: 0.97358 valid_1's auc: 0.908882 [1200] training's auc: 0.976528 valid_1's auc: 0.908947 [1300] training's auc: 0.979035 valid_1's auc: 0.90898 [1400] training's auc: 0.981294 valid_1's auc: 0.908869 Early stopping, best iteration is: [1315] training's auc: 0.979415 valid_1's auc: 0.909043 Partial score of fold 0 is: 0.9090426398020737 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.908537 valid_1's auc: 0.887786 [200] training's auc: 0.923184 valid_1's auc: 0.896164 [300] training's auc: 0.933263 valid_1's auc: 0.900426 [400] training's auc: 0.941014 valid_1's auc: 0.902331 [500] training's auc: 0.947691 valid_1's auc: 0.903619 [600] training's auc: 0.953611 valid_1's auc: 0.904335 [700] training's auc: 0.958697 valid_1's auc: 0.904808 [800] training's auc: 0.963293 valid_1's auc: 0.905162 [900] training's auc: 0.967137 valid_1's auc: 0.905382 [1000] training's auc: 0.97073 valid_1's auc: 0.90566 [1100] training's auc: 0.973923 valid_1's auc: 0.90561 Early stopping, best iteration is: [1038] training's auc: 0.971941 valid_1's auc: 0.9057 Partial score of fold 1 is: 0.9056998056618878 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.908866 valid_1's auc: 0.885518 [200] training's auc: 0.923802 valid_1's auc: 0.893547 [300] training's auc: 0.933845 valid_1's auc: 0.897436 [400] training's auc: 0.941673 valid_1's auc: 0.898867 [500] training's auc: 0.948531 valid_1's auc: 0.89974 [600] training's auc: 0.954232 valid_1's auc: 0.900702 [700] training's auc: 0.959143 valid_1's auc: 0.901135 [800] training's auc: 0.96357 valid_1's auc: 0.901402 Early stopping, best iteration is: [769] training's auc: 0.962195 valid_1's auc: 0.901516 Partial score of fold 2 is: 0.9015157694296652 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.908786 valid_1's auc: 0.891511 [200] training's auc: 0.923641 valid_1's auc: 0.898708 [300] training's auc: 0.933698 valid_1's auc: 0.902063 [400] training's auc: 0.941417 valid_1's auc: 0.903723 [500] training's auc: 0.947947 valid_1's auc: 0.904799 [600] training's auc: 0.953893 valid_1's auc: 0.905344 [700] training's auc: 0.958967 valid_1's auc: 0.905738 [800] training's auc: 0.963422 valid_1's auc: 0.906055 Early stopping, best iteration is: [796] training's auc: 0.963266 valid_1's auc: 0.906075 Partial score of fold 3 is: 0.9060751132974743 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.908077 valid_1's auc: 0.888192 [200] training's auc: 0.922997 valid_1's auc: 0.89722 [300] training's auc: 0.933057 valid_1's auc: 0.901585 [400] training's auc: 0.940796 valid_1's auc: 0.903284 [500] training's auc: 0.947586 valid_1's auc: 0.904788 [600] training's auc: 0.953461 valid_1's auc: 0.905619 [700] training's auc: 0.958473 valid_1's auc: 0.905907 [800] training's auc: 0.962865 valid_1's auc: 0.906373 [900] training's auc: 0.966964 valid_1's auc: 0.906583 [1000] training's auc: 0.970526 valid_1's auc: 0.906737 [1100] training's auc: 0.973654 valid_1's auc: 0.906723 [1200] training's auc: 0.976443 valid_1's auc: 0.906708 Early stopping, best iteration is: [1128] training's auc: 0.974491 valid_1's auc: 0.906884 Partial score of fold 4 is: 0.9068841749996239 Our oof AUC score is: 0.9058214006407159 auc: 0.9058214006407159 | 27 | 0.9058 | 0.7671 | 4.621 | 4.803 | 0.01787 | 16.01 | 1.036 | 1.079 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.897702 valid_1's auc: 0.885156 [200] training's auc: 0.905142 valid_1's auc: 0.88931 [300] training's auc: 0.911545 valid_1's auc: 0.892411 [400] training's auc: 0.917076 valid_1's auc: 0.895346 [500] training's auc: 0.92186 valid_1's auc: 0.897907 [600] training's auc: 0.925875 valid_1's auc: 0.899846 [700] training's auc: 0.929499 valid_1's auc: 0.901336 [800] training's auc: 0.932624 valid_1's auc: 0.902459 [900] training's auc: 0.935602 valid_1's auc: 0.903265 [1000] training's auc: 0.938306 valid_1's auc: 0.904114 [1100] training's auc: 0.940932 valid_1's auc: 0.904802 [1200] training's auc: 0.943343 valid_1's auc: 0.905374 [1300] training's auc: 0.945782 valid_1's auc: 0.905854 [1400] training's auc: 0.948036 valid_1's auc: 0.906245 [1500] training's auc: 0.950289 valid_1's auc: 0.906604 [1600] training's auc: 0.952326 valid_1's auc: 0.907008 [1700] training's auc: 0.954317 valid_1's auc: 0.907361 [1800] training's auc: 0.956206 valid_1's auc: 0.90759 [1900] training's auc: 0.958 valid_1's auc: 0.907802 [2000] training's auc: 0.959737 valid_1's auc: 0.907964 [2100] training's auc: 0.961361 valid_1's auc: 0.908111 [2200] training's auc: 0.96288 valid_1's auc: 0.908235 [2300] training's auc: 0.964372 valid_1's auc: 0.908481 [2400] training's auc: 0.965852 valid_1's auc: 0.908639 [2500] training's auc: 0.96724 valid_1's auc: 0.908794 [2600] training's auc: 0.968523 valid_1's auc: 0.908861 [2700] training's auc: 0.969746 valid_1's auc: 0.908978 [2800] training's auc: 0.970945 valid_1's auc: 0.909035 [2900] training's auc: 0.972142 valid_1's auc: 0.909085 [3000] training's auc: 0.973263 valid_1's auc: 0.909174 [3100] training's auc: 0.974338 valid_1's auc: 0.909198 [3200] training's auc: 0.975318 valid_1's auc: 0.909265 Early stopping, best iteration is: [3193] training's auc: 0.975252 valid_1's auc: 0.909269 Partial score of fold 0 is: 0.9092687896610315 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.898199 valid_1's auc: 0.882233 [200] training's auc: 0.905683 valid_1's auc: 0.886767 [300] training's auc: 0.912295 valid_1's auc: 0.890263 [400] training's auc: 0.917666 valid_1's auc: 0.893102 [500] training's auc: 0.922504 valid_1's auc: 0.895868 [600] training's auc: 0.926654 valid_1's auc: 0.897821 [700] training's auc: 0.930152 valid_1's auc: 0.899393 [800] training's auc: 0.933277 valid_1's auc: 0.900604 [900] training's auc: 0.936246 valid_1's auc: 0.901449 [1000] training's auc: 0.938978 valid_1's auc: 0.902213 [1100] training's auc: 0.941524 valid_1's auc: 0.902868 [1200] training's auc: 0.943968 valid_1's auc: 0.903509 [1300] training's auc: 0.946338 valid_1's auc: 0.904001 [1400] training's auc: 0.948592 valid_1's auc: 0.904396 [1500] training's auc: 0.950823 valid_1's auc: 0.904713 [1600] training's auc: 0.952867 valid_1's auc: 0.904941 [1700] training's auc: 0.95488 valid_1's auc: 0.905198 [1800] training's auc: 0.956721 valid_1's auc: 0.905441 [1900] training's auc: 0.958589 valid_1's auc: 0.905703 [2000] training's auc: 0.960225 valid_1's auc: 0.905946 [2100] training's auc: 0.961852 valid_1's auc: 0.906004 [2200] training's auc: 0.963447 valid_1's auc: 0.906143 [2300] training's auc: 0.964922 valid_1's auc: 0.906233 [2400] training's auc: 0.966358 valid_1's auc: 0.906344 [2500] training's auc: 0.967748 valid_1's auc: 0.906484 [2600] training's auc: 0.969039 valid_1's auc: 0.9066 [2700] training's auc: 0.97029 valid_1's auc: 0.906715 [2800] training's auc: 0.971485 valid_1's auc: 0.906823 Early stopping, best iteration is: [2795] training's auc: 0.971422 valid_1's auc: 0.906836 Partial score of fold 1 is: 0.906836397161367 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.899387 valid_1's auc: 0.880496 [200] training's auc: 0.906424 valid_1's auc: 0.884556 [300] training's auc: 0.912652 valid_1's auc: 0.887948 [400] training's auc: 0.918055 valid_1's auc: 0.89079 [500] training's auc: 0.922782 valid_1's auc: 0.893557 [600] training's auc: 0.92681 valid_1's auc: 0.895549 [700] training's auc: 0.930328 valid_1's auc: 0.896961 [800] training's auc: 0.933489 valid_1's auc: 0.898086 [900] training's auc: 0.936437 valid_1's auc: 0.898802 [1000] training's auc: 0.939137 valid_1's auc: 0.899504 [1100] training's auc: 0.941755 valid_1's auc: 0.90009 [1200] training's auc: 0.944228 valid_1's auc: 0.90044 [1300] training's auc: 0.946574 valid_1's auc: 0.900757 [1400] training's auc: 0.948792 valid_1's auc: 0.901081 [1500] training's auc: 0.95093 valid_1's auc: 0.901448 [1600] training's auc: 0.95301 valid_1's auc: 0.90179 [1700] training's auc: 0.954975 valid_1's auc: 0.902023 [1800] training's auc: 0.956793 valid_1's auc: 0.902227 [1900] training's auc: 0.958641 valid_1's auc: 0.90243 [2000] training's auc: 0.960329 valid_1's auc: 0.902657 [2100] training's auc: 0.961958 valid_1's auc: 0.902724 [2200] training's auc: 0.963462 valid_1's auc: 0.90287 [2300] training's auc: 0.96498 valid_1's auc: 0.903003 [2400] training's auc: 0.96637 valid_1's auc: 0.903066 [2500] training's auc: 0.967778 valid_1's auc: 0.903135 [2600] training's auc: 0.969063 valid_1's auc: 0.903241 [2700] training's auc: 0.970278 valid_1's auc: 0.903289 [2800] training's auc: 0.97154 valid_1's auc: 0.903296 [2900] training's auc: 0.972693 valid_1's auc: 0.903374 Early stopping, best iteration is: [2896] training's auc: 0.972647 valid_1's auc: 0.903381 Partial score of fold 2 is: 0.9033810534663497 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.898153 valid_1's auc: 0.88609 [200] training's auc: 0.905307 valid_1's auc: 0.890046 [300] training's auc: 0.911783 valid_1's auc: 0.893143 [400] training's auc: 0.917409 valid_1's auc: 0.896131 [500] training's auc: 0.922267 valid_1's auc: 0.898161 [600] training's auc: 0.926324 valid_1's auc: 0.899753 [700] training's auc: 0.929872 valid_1's auc: 0.900936 [800] training's auc: 0.933072 valid_1's auc: 0.901949 [900] training's auc: 0.936091 valid_1's auc: 0.902726 [1000] training's auc: 0.938847 valid_1's auc: 0.903293 [1100] training's auc: 0.94154 valid_1's auc: 0.903836 [1200] training's auc: 0.944032 valid_1's auc: 0.904241 [1300] training's auc: 0.946338 valid_1's auc: 0.904651 [1400] training's auc: 0.948586 valid_1's auc: 0.904946 [1500] training's auc: 0.950794 valid_1's auc: 0.905215 [1600] training's auc: 0.95283 valid_1's auc: 0.905387 [1700] training's auc: 0.954845 valid_1's auc: 0.905626 [1800] training's auc: 0.956753 valid_1's auc: 0.90576 [1900] training's auc: 0.958579 valid_1's auc: 0.905831 [2000] training's auc: 0.960281 valid_1's auc: 0.905901 [2100] training's auc: 0.961967 valid_1's auc: 0.905981 [2200] training's auc: 0.963454 valid_1's auc: 0.906105 [2300] training's auc: 0.96491 valid_1's auc: 0.906214 Early stopping, best iteration is: [2299] training's auc: 0.964897 valid_1's auc: 0.906218 Partial score of fold 3 is: 0.9062178223733378 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.898517 valid_1's auc: 0.882938 [200] training's auc: 0.905466 valid_1's auc: 0.88718 [300] training's auc: 0.91155 valid_1's auc: 0.890622 [400] training's auc: 0.916937 valid_1's auc: 0.893911 [500] training's auc: 0.921711 valid_1's auc: 0.896976 [600] training's auc: 0.925875 valid_1's auc: 0.899321 [700] training's auc: 0.929467 valid_1's auc: 0.900883 [800] training's auc: 0.932619 valid_1's auc: 0.90208 [900] training's auc: 0.935645 valid_1's auc: 0.902955 [1000] training's auc: 0.938469 valid_1's auc: 0.903752 [1100] training's auc: 0.941134 valid_1's auc: 0.904332 [1200] training's auc: 0.943587 valid_1's auc: 0.904991 [1300] training's auc: 0.945975 valid_1's auc: 0.905433 [1400] training's auc: 0.948306 valid_1's auc: 0.905747 [1500] training's auc: 0.950487 valid_1's auc: 0.906139 [1600] training's auc: 0.952618 valid_1's auc: 0.906418 [1700] training's auc: 0.954644 valid_1's auc: 0.906691 [1800] training's auc: 0.956531 valid_1's auc: 0.906997 [1900] training's auc: 0.958322 valid_1's auc: 0.90728 [2000] training's auc: 0.960024 valid_1's auc: 0.907403 [2100] training's auc: 0.961675 valid_1's auc: 0.90748 [2200] training's auc: 0.963252 valid_1's auc: 0.907669 [2300] training's auc: 0.964726 valid_1's auc: 0.907776 [2400] training's auc: 0.966133 valid_1's auc: 0.907902 [2500] training's auc: 0.967493 valid_1's auc: 0.908013 [2600] training's auc: 0.968822 valid_1's auc: 0.908042 [2700] training's auc: 0.970084 valid_1's auc: 0.908188 [2800] training's auc: 0.97127 valid_1's auc: 0.908235 [2900] training's auc: 0.972439 valid_1's auc: 0.908273 Early stopping, best iteration is: [2893] training's auc: 0.972358 valid_1's auc: 0.908286 Partial score of fold 4 is: 0.9082858223810274 Our oof AUC score is: 0.9067461313919523 auc: 0.9067461313919523 | 28 | 0.9067 | 0.6594 | 4.883 | 4.989 | 0.006024 | 16.74 | 1.51 | 1.039 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.908265 valid_1's auc: 0.890778 [200] training's auc: 0.922089 valid_1's auc: 0.8983 [300] training's auc: 0.931866 valid_1's auc: 0.902161 [400] training's auc: 0.93978 valid_1's auc: 0.904473 [500] training's auc: 0.94656 valid_1's auc: 0.905546 [600] training's auc: 0.952498 valid_1's auc: 0.906734 [700] training's auc: 0.957917 valid_1's auc: 0.90759 [800] training's auc: 0.962562 valid_1's auc: 0.908246 [900] training's auc: 0.966879 valid_1's auc: 0.908699 [1000] training's auc: 0.970424 valid_1's auc: 0.908813 [1100] training's auc: 0.973679 valid_1's auc: 0.90906 [1200] training's auc: 0.9766 valid_1's auc: 0.90908 Early stopping, best iteration is: [1156] training's auc: 0.975321 valid_1's auc: 0.909098 Partial score of fold 0 is: 0.9090980842091614 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.908547 valid_1's auc: 0.888723 [200] training's auc: 0.922251 valid_1's auc: 0.896429 [300] training's auc: 0.932186 valid_1's auc: 0.9009 [400] training's auc: 0.939869 valid_1's auc: 0.903173 [500] training's auc: 0.946789 valid_1's auc: 0.904644 [600] training's auc: 0.952808 valid_1's auc: 0.905736 [700] training's auc: 0.958052 valid_1's auc: 0.906282 [800] training's auc: 0.96291 valid_1's auc: 0.906753 [900] training's auc: 0.967181 valid_1's auc: 0.906935 [1000] training's auc: 0.970843 valid_1's auc: 0.907035 [1100] training's auc: 0.974024 valid_1's auc: 0.907343 [1200] training's auc: 0.977062 valid_1's auc: 0.907537 [1300] training's auc: 0.979635 valid_1's auc: 0.90755 Early stopping, best iteration is: [1227] training's auc: 0.977821 valid_1's auc: 0.907609 Partial score of fold 1 is: 0.9076093019959987 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.909108 valid_1's auc: 0.886255 [200] training's auc: 0.9228 valid_1's auc: 0.893402 [300] training's auc: 0.932785 valid_1's auc: 0.897745 [400] training's auc: 0.9406 valid_1's auc: 0.899737 [500] training's auc: 0.947384 valid_1's auc: 0.901275 [600] training's auc: 0.953249 valid_1's auc: 0.901932 [700] training's auc: 0.958543 valid_1's auc: 0.902527 [800] training's auc: 0.963008 valid_1's auc: 0.90282 Early stopping, best iteration is: [760] training's auc: 0.961296 valid_1's auc: 0.90286 Partial score of fold 2 is: 0.902859815733095 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.908703 valid_1's auc: 0.891953 [200] training's auc: 0.922413 valid_1's auc: 0.898508 [300] training's auc: 0.932361 valid_1's auc: 0.901869 [400] training's auc: 0.940227 valid_1's auc: 0.903782 [500] training's auc: 0.946892 valid_1's auc: 0.904763 [600] training's auc: 0.953066 valid_1's auc: 0.905551 [700] training's auc: 0.958242 valid_1's auc: 0.905933 [800] training's auc: 0.962888 valid_1's auc: 0.906309 [900] training's auc: 0.967273 valid_1's auc: 0.906489 Early stopping, best iteration is: [880] training's auc: 0.966459 valid_1's auc: 0.906588 Partial score of fold 3 is: 0.9065884664153262 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.908507 valid_1's auc: 0.889904 [200] training's auc: 0.922099 valid_1's auc: 0.897733 [300] training's auc: 0.932003 valid_1's auc: 0.902166 [400] training's auc: 0.939791 valid_1's auc: 0.904581 [500] training's auc: 0.946591 valid_1's auc: 0.905852 [600] training's auc: 0.952642 valid_1's auc: 0.90677 [700] training's auc: 0.957906 valid_1's auc: 0.907341 [800] training's auc: 0.962536 valid_1's auc: 0.907828 [900] training's auc: 0.966731 valid_1's auc: 0.908164 [1000] training's auc: 0.970434 valid_1's auc: 0.908287 [1100] training's auc: 0.973682 valid_1's auc: 0.90836 [1200] training's auc: 0.976747 valid_1's auc: 0.908641 [1300] training's auc: 0.979512 valid_1's auc: 0.908606 Early stopping, best iteration is: [1210] training's auc: 0.977049 valid_1's auc: 0.908703 Partial score of fold 4 is: 0.9087031314615576 Our oof AUC score is: 0.9069602265424341 auc: 0.9069602265424341 | 29 | 0.907 | 0.6403 | 0.3946 | 4.887 | 0.01584 | 16.7 | 1.12 | 1.153 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.891333 valid_1's auc: 0.878058 [200] training's auc: 0.899732 valid_1's auc: 0.884128 [300] training's auc: 0.90646 valid_1's auc: 0.887876 [400] training's auc: 0.912152 valid_1's auc: 0.890774 [500] training's auc: 0.91705 valid_1's auc: 0.89347 [600] training's auc: 0.921557 valid_1's auc: 0.895936 [700] training's auc: 0.925459 valid_1's auc: 0.897915 [800] training's auc: 0.928715 valid_1's auc: 0.899359 [900] training's auc: 0.931738 valid_1's auc: 0.900481 [1000] training's auc: 0.934468 valid_1's auc: 0.90148 [1100] training's auc: 0.936937 valid_1's auc: 0.902233 [1200] training's auc: 0.939326 valid_1's auc: 0.90296 [1300] training's auc: 0.941541 valid_1's auc: 0.903433 [1400] training's auc: 0.943631 valid_1's auc: 0.903908 [1500] training's auc: 0.945674 valid_1's auc: 0.904378 [1600] training's auc: 0.947665 valid_1's auc: 0.904789 [1700] training's auc: 0.949474 valid_1's auc: 0.90508 [1800] training's auc: 0.951337 valid_1's auc: 0.905245 [1900] training's auc: 0.953027 valid_1's auc: 0.905596 [2000] training's auc: 0.954708 valid_1's auc: 0.905794 [2100] training's auc: 0.956239 valid_1's auc: 0.905996 [2200] training's auc: 0.957785 valid_1's auc: 0.906215 [2300] training's auc: 0.959247 valid_1's auc: 0.906465 [2400] training's auc: 0.960669 valid_1's auc: 0.9066 [2500] training's auc: 0.96205 valid_1's auc: 0.906802 [2600] training's auc: 0.96335 valid_1's auc: 0.906922 [2700] training's auc: 0.964589 valid_1's auc: 0.907017 [2800] training's auc: 0.965786 valid_1's auc: 0.907133 [2900] training's auc: 0.966999 valid_1's auc: 0.907199 [3000] training's auc: 0.968169 valid_1's auc: 0.907296 Early stopping, best iteration is: [2975] training's auc: 0.967875 valid_1's auc: 0.907316 Partial score of fold 0 is: 0.9073160987038598 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.892346 valid_1's auc: 0.876736 [200] training's auc: 0.900276 valid_1's auc: 0.881583 [300] training's auc: 0.906675 valid_1's auc: 0.885374 [400] training's auc: 0.912443 valid_1's auc: 0.888303 [500] training's auc: 0.917509 valid_1's auc: 0.891351 [600] training's auc: 0.922014 valid_1's auc: 0.89395 [700] training's auc: 0.925866 valid_1's auc: 0.896018 [800] training's auc: 0.929253 valid_1's auc: 0.897381 [900] training's auc: 0.932244 valid_1's auc: 0.898599 [1000] training's auc: 0.934919 valid_1's auc: 0.899559 [1100] training's auc: 0.937427 valid_1's auc: 0.900361 [1200] training's auc: 0.939818 valid_1's auc: 0.900963 [1300] training's auc: 0.942058 valid_1's auc: 0.901445 [1400] training's auc: 0.944186 valid_1's auc: 0.901931 [1500] training's auc: 0.946247 valid_1's auc: 0.902428 [1600] training's auc: 0.948228 valid_1's auc: 0.902727 [1700] training's auc: 0.950023 valid_1's auc: 0.902984 [1800] training's auc: 0.951801 valid_1's auc: 0.903294 [1900] training's auc: 0.953541 valid_1's auc: 0.903601 [2000] training's auc: 0.955211 valid_1's auc: 0.903864 [2100] training's auc: 0.956767 valid_1's auc: 0.904029 [2200] training's auc: 0.95831 valid_1's auc: 0.904132 [2300] training's auc: 0.959775 valid_1's auc: 0.904268 [2400] training's auc: 0.961201 valid_1's auc: 0.904356 [2500] training's auc: 0.962562 valid_1's auc: 0.904465 [2600] training's auc: 0.963873 valid_1's auc: 0.904531 [2700] training's auc: 0.965105 valid_1's auc: 0.904552 [2800] training's auc: 0.966324 valid_1's auc: 0.904598 [2900] training's auc: 0.967479 valid_1's auc: 0.904599 [3000] training's auc: 0.968538 valid_1's auc: 0.904656 [3100] training's auc: 0.96962 valid_1's auc: 0.904663 [3200] training's auc: 0.970673 valid_1's auc: 0.904746 [3300] training's auc: 0.971642 valid_1's auc: 0.9048 [3400] training's auc: 0.972587 valid_1's auc: 0.904823 Early stopping, best iteration is: [3386] training's auc: 0.97246 valid_1's auc: 0.904841 Partial score of fold 1 is: 0.9048408131142794 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.89287 valid_1's auc: 0.87563 [200] training's auc: 0.901278 valid_1's auc: 0.8804 [300] training's auc: 0.907518 valid_1's auc: 0.883654 [400] training's auc: 0.913135 valid_1's auc: 0.886801 [500] training's auc: 0.917731 valid_1's auc: 0.889265 [600] training's auc: 0.922148 valid_1's auc: 0.891695 [700] training's auc: 0.925907 valid_1's auc: 0.893697 [800] training's auc: 0.92929 valid_1's auc: 0.895049 [900] training's auc: 0.932287 valid_1's auc: 0.896157 [1000] training's auc: 0.934942 valid_1's auc: 0.897066 [1100] training's auc: 0.937475 valid_1's auc: 0.897738 [1200] training's auc: 0.939901 valid_1's auc: 0.898262 [1300] training's auc: 0.942137 valid_1's auc: 0.898625 [1400] training's auc: 0.944218 valid_1's auc: 0.899047 [1500] training's auc: 0.946227 valid_1's auc: 0.899396 [1600] training's auc: 0.948203 valid_1's auc: 0.899639 [1700] training's auc: 0.950093 valid_1's auc: 0.899902 [1800] training's auc: 0.951893 valid_1's auc: 0.90013 [1900] training's auc: 0.953656 valid_1's auc: 0.900375 [2000] training's auc: 0.955287 valid_1's auc: 0.90048 [2100] training's auc: 0.956887 valid_1's auc: 0.900598 [2200] training's auc: 0.958384 valid_1's auc: 0.900765 [2300] training's auc: 0.959846 valid_1's auc: 0.900923 [2400] training's auc: 0.961195 valid_1's auc: 0.900974 [2500] training's auc: 0.962572 valid_1's auc: 0.900999 Early stopping, best iteration is: [2408] training's auc: 0.961314 valid_1's auc: 0.901012 Partial score of fold 2 is: 0.9010123975352681 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.891784 valid_1's auc: 0.8824 [200] training's auc: 0.899663 valid_1's auc: 0.887107 [300] training's auc: 0.90657 valid_1's auc: 0.889657 [400] training's auc: 0.912504 valid_1's auc: 0.892491 [500] training's auc: 0.917349 valid_1's auc: 0.894724 [600] training's auc: 0.921965 valid_1's auc: 0.89676 [700] training's auc: 0.925854 valid_1's auc: 0.898291 [800] training's auc: 0.929205 valid_1's auc: 0.899454 [900] training's auc: 0.932205 valid_1's auc: 0.900315 [1000] training's auc: 0.934961 valid_1's auc: 0.901067 [1100] training's auc: 0.937431 valid_1's auc: 0.901548 [1200] training's auc: 0.939843 valid_1's auc: 0.902116 [1300] training's auc: 0.942111 valid_1's auc: 0.902504 [1400] training's auc: 0.944267 valid_1's auc: 0.902885 [1500] training's auc: 0.946349 valid_1's auc: 0.903228 [1600] training's auc: 0.948361 valid_1's auc: 0.90339 [1700] training's auc: 0.950238 valid_1's auc: 0.903584 [1800] training's auc: 0.952046 valid_1's auc: 0.903855 [1900] training's auc: 0.953848 valid_1's auc: 0.903963 [2000] training's auc: 0.955521 valid_1's auc: 0.90411 [2100] training's auc: 0.957149 valid_1's auc: 0.904196 [2200] training's auc: 0.958648 valid_1's auc: 0.904327 [2300] training's auc: 0.960085 valid_1's auc: 0.904394 [2400] training's auc: 0.961519 valid_1's auc: 0.904397 Early stopping, best iteration is: [2348] training's auc: 0.960793 valid_1's auc: 0.904441 Partial score of fold 3 is: 0.9044406684378524 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.891653 valid_1's auc: 0.877568 [200] training's auc: 0.899792 valid_1's auc: 0.882898 [300] training's auc: 0.906243 valid_1's auc: 0.886354 [400] training's auc: 0.911845 valid_1's auc: 0.889546 [500] training's auc: 0.916721 valid_1's auc: 0.892351 [600] training's auc: 0.921275 valid_1's auc: 0.895153 [700] training's auc: 0.925218 valid_1's auc: 0.897425 [800] training's auc: 0.928663 valid_1's auc: 0.899087 [900] training's auc: 0.931684 valid_1's auc: 0.900365 [1000] training's auc: 0.934446 valid_1's auc: 0.901322 [1100] training's auc: 0.937012 valid_1's auc: 0.901999 [1200] training's auc: 0.939412 valid_1's auc: 0.90261 [1300] training's auc: 0.941649 valid_1's auc: 0.903145 [1400] training's auc: 0.943834 valid_1's auc: 0.90359 [1500] training's auc: 0.945858 valid_1's auc: 0.904039 [1600] training's auc: 0.947864 valid_1's auc: 0.904413 [1700] training's auc: 0.949763 valid_1's auc: 0.904584 [1800] training's auc: 0.951581 valid_1's auc: 0.904969 [1900] training's auc: 0.953296 valid_1's auc: 0.905244 [2000] training's auc: 0.954949 valid_1's auc: 0.905463 [2100] training's auc: 0.956523 valid_1's auc: 0.905644 [2200] training's auc: 0.958053 valid_1's auc: 0.905869 [2300] training's auc: 0.959543 valid_1's auc: 0.905985 [2400] training's auc: 0.960927 valid_1's auc: 0.906076 [2500] training's auc: 0.962311 valid_1's auc: 0.906183 [2600] training's auc: 0.963639 valid_1's auc: 0.9062 [2700] training's auc: 0.964885 valid_1's auc: 0.906326 [2800] training's auc: 0.966127 valid_1's auc: 0.906435 [2900] training's auc: 0.967198 valid_1's auc: 0.906501 [3000] training's auc: 0.9683 valid_1's auc: 0.906566 [3100] training's auc: 0.969397 valid_1's auc: 0.906628 [3200] training's auc: 0.970447 valid_1's auc: 0.906696 [3300] training's auc: 0.971429 valid_1's auc: 0.906687 Early stopping, best iteration is: [3207] training's auc: 0.970516 valid_1's auc: 0.906706 Partial score of fold 4 is: 0.9067055813383872 Our oof AUC score is: 0.9048373131808696 auc: 0.9048373131808696 | 30 | 0.9048 | 0.9417 | 4.621 | 4.761 | 0.005161 | 16.9 | 1.023 | 1.552 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.898655 valid_1's auc: 0.885387 [200] training's auc: 0.907886 valid_1's auc: 0.890538 [300] training's auc: 0.91534 valid_1's auc: 0.894171 [400] training's auc: 0.921941 valid_1's auc: 0.897801 [500] training's auc: 0.927383 valid_1's auc: 0.900532 [600] training's auc: 0.931958 valid_1's auc: 0.902341 [700] training's auc: 0.93613 valid_1's auc: 0.903663 [800] training's auc: 0.939877 valid_1's auc: 0.904743 [900] training's auc: 0.943338 valid_1's auc: 0.90546 [1000] training's auc: 0.94652 valid_1's auc: 0.906109 [1100] training's auc: 0.94962 valid_1's auc: 0.906575 [1200] training's auc: 0.952558 valid_1's auc: 0.907122 [1300] training's auc: 0.955272 valid_1's auc: 0.90749 [1400] training's auc: 0.9578 valid_1's auc: 0.907919 [1500] training's auc: 0.960266 valid_1's auc: 0.90819 [1600] training's auc: 0.962519 valid_1's auc: 0.908425 [1700] training's auc: 0.964646 valid_1's auc: 0.908643 [1800] training's auc: 0.966701 valid_1's auc: 0.908754 [1900] training's auc: 0.968612 valid_1's auc: 0.908875 [2000] training's auc: 0.970393 valid_1's auc: 0.909064 [2100] training's auc: 0.972102 valid_1's auc: 0.909184 [2200] training's auc: 0.973676 valid_1's auc: 0.909242 [2300] training's auc: 0.975214 valid_1's auc: 0.909308 [2400] training's auc: 0.976716 valid_1's auc: 0.909426 [2500] training's auc: 0.978089 valid_1's auc: 0.909487 [2600] training's auc: 0.979339 valid_1's auc: 0.90959 [2700] training's auc: 0.980507 valid_1's auc: 0.909615 [2800] training's auc: 0.981676 valid_1's auc: 0.909647 [2900] training's auc: 0.982798 valid_1's auc: 0.909679 [3000] training's auc: 0.983825 valid_1's auc: 0.90969 Early stopping, best iteration is: [2928] training's auc: 0.983091 valid_1's auc: 0.909722 Partial score of fold 0 is: 0.9097218055201672 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.899331 valid_1's auc: 0.883205 [200] training's auc: 0.90843 valid_1's auc: 0.888859 [300] training's auc: 0.91596 valid_1's auc: 0.892746 [400] training's auc: 0.922517 valid_1's auc: 0.896316 [500] training's auc: 0.927834 valid_1's auc: 0.898833 [600] training's auc: 0.932394 valid_1's auc: 0.900607 [700] training's auc: 0.936397 valid_1's auc: 0.901798 [800] training's auc: 0.940135 valid_1's auc: 0.902838 [900] training's auc: 0.943728 valid_1's auc: 0.903528 [1000] training's auc: 0.946921 valid_1's auc: 0.904301 [1100] training's auc: 0.949958 valid_1's auc: 0.904862 [1200] training's auc: 0.95289 valid_1's auc: 0.905434 [1300] training's auc: 0.955638 valid_1's auc: 0.905721 [1400] training's auc: 0.958253 valid_1's auc: 0.905976 [1500] training's auc: 0.960728 valid_1's auc: 0.906187 [1600] training's auc: 0.962999 valid_1's auc: 0.906363 [1700] training's auc: 0.965139 valid_1's auc: 0.906452 [1800] training's auc: 0.967109 valid_1's auc: 0.906583 [1900] training's auc: 0.968996 valid_1's auc: 0.90675 [2000] training's auc: 0.970757 valid_1's auc: 0.906912 [2100] training's auc: 0.972435 valid_1's auc: 0.906947 [2200] training's auc: 0.974066 valid_1's auc: 0.907018 Early stopping, best iteration is: [2166] training's auc: 0.973507 valid_1's auc: 0.907049 Partial score of fold 1 is: 0.9070493549452189 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.90022 valid_1's auc: 0.881589 [200] training's auc: 0.908853 valid_1's auc: 0.886179 [300] training's auc: 0.916354 valid_1's auc: 0.890488 [400] training's auc: 0.922739 valid_1's auc: 0.894046 [500] training's auc: 0.928092 valid_1's auc: 0.896515 [600] training's auc: 0.932649 valid_1's auc: 0.898218 [700] training's auc: 0.936714 valid_1's auc: 0.899347 [800] training's auc: 0.940409 valid_1's auc: 0.900131 [900] training's auc: 0.943962 valid_1's auc: 0.900767 [1000] training's auc: 0.947161 valid_1's auc: 0.901296 [1100] training's auc: 0.95021 valid_1's auc: 0.901727 [1200] training's auc: 0.953202 valid_1's auc: 0.902184 [1300] training's auc: 0.95599 valid_1's auc: 0.902452 [1400] training's auc: 0.958474 valid_1's auc: 0.902826 [1500] training's auc: 0.960895 valid_1's auc: 0.903055 [1600] training's auc: 0.96317 valid_1's auc: 0.903243 [1700] training's auc: 0.965322 valid_1's auc: 0.903413 [1800] training's auc: 0.967345 valid_1's auc: 0.903565 [1900] training's auc: 0.969249 valid_1's auc: 0.90371 [2000] training's auc: 0.970992 valid_1's auc: 0.903796 [2100] training's auc: 0.972643 valid_1's auc: 0.903795 Early stopping, best iteration is: [2020] training's auc: 0.971354 valid_1's auc: 0.903817 Partial score of fold 2 is: 0.9038170703944204 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.898934 valid_1's auc: 0.887186 [200] training's auc: 0.907784 valid_1's auc: 0.891789 [300] training's auc: 0.915539 valid_1's auc: 0.895476 [400] training's auc: 0.922322 valid_1's auc: 0.898435 [500] training's auc: 0.927716 valid_1's auc: 0.900297 [600] training's auc: 0.932341 valid_1's auc: 0.901743 [700] training's auc: 0.93647 valid_1's auc: 0.902816 [800] training's auc: 0.940274 valid_1's auc: 0.90347 [900] training's auc: 0.943865 valid_1's auc: 0.904059 [1000] training's auc: 0.947175 valid_1's auc: 0.904654 [1100] training's auc: 0.950388 valid_1's auc: 0.904928 [1200] training's auc: 0.95338 valid_1's auc: 0.905198 [1300] training's auc: 0.956143 valid_1's auc: 0.905486 [1400] training's auc: 0.958752 valid_1's auc: 0.905644 [1500] training's auc: 0.961172 valid_1's auc: 0.905876 [1600] training's auc: 0.963402 valid_1's auc: 0.906048 [1700] training's auc: 0.965506 valid_1's auc: 0.906272 [1800] training's auc: 0.967485 valid_1's auc: 0.906432 [1900] training's auc: 0.969481 valid_1's auc: 0.906488 [2000] training's auc: 0.971253 valid_1's auc: 0.906597 [2100] training's auc: 0.972965 valid_1's auc: 0.906646 [2200] training's auc: 0.974538 valid_1's auc: 0.90668 [2300] training's auc: 0.976014 valid_1's auc: 0.906693 [2400] training's auc: 0.977385 valid_1's auc: 0.90673 [2500] training's auc: 0.978802 valid_1's auc: 0.906778 [2600] training's auc: 0.98002 valid_1's auc: 0.906832 [2700] training's auc: 0.981179 valid_1's auc: 0.906862 Early stopping, best iteration is: [2693] training's auc: 0.981102 valid_1's auc: 0.906884 Partial score of fold 3 is: 0.9068835718973489 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.899114 valid_1's auc: 0.884592 [200] training's auc: 0.907768 valid_1's auc: 0.889387 [300] training's auc: 0.915178 valid_1's auc: 0.893431 [400] training's auc: 0.921782 valid_1's auc: 0.897442 [500] training's auc: 0.927283 valid_1's auc: 0.900448 [600] training's auc: 0.931974 valid_1's auc: 0.902337 [700] training's auc: 0.936159 valid_1's auc: 0.903491 [800] training's auc: 0.939885 valid_1's auc: 0.904486 [900] training's auc: 0.943541 valid_1's auc: 0.905253 [1000] training's auc: 0.946854 valid_1's auc: 0.905901 [1100] training's auc: 0.949891 valid_1's auc: 0.906524 [1200] training's auc: 0.952882 valid_1's auc: 0.907077 [1300] training's auc: 0.955659 valid_1's auc: 0.907399 [1400] training's auc: 0.958193 valid_1's auc: 0.907727 [1500] training's auc: 0.960663 valid_1's auc: 0.907959 [1600] training's auc: 0.962934 valid_1's auc: 0.908127 [1700] training's auc: 0.96505 valid_1's auc: 0.908364 [1800] training's auc: 0.967053 valid_1's auc: 0.90857 [1900] training's auc: 0.968974 valid_1's auc: 0.908777 [2000] training's auc: 0.970691 valid_1's auc: 0.908905 [2100] training's auc: 0.972472 valid_1's auc: 0.908912 [2200] training's auc: 0.974028 valid_1's auc: 0.90893 [2300] training's auc: 0.975516 valid_1's auc: 0.908982 [2400] training's auc: 0.976965 valid_1's auc: 0.908977 Early stopping, best iteration is: [2333] training's auc: 0.976012 valid_1's auc: 0.909023 Partial score of fold 4 is: 0.9090229264429703 Our oof AUC score is: 0.9072497987482065 auc: 0.9072497987482065 | 31 | 0.9072 | 0.636 | 0.04764 | 4.776 | 0.007769 | 16.56 | 1.119 | 1.532 | Training until validation scores don't improve for 100 rounds [100] training's auc: 0.900973 valid_1's auc: 0.884952 [200] training's auc: 0.912573 valid_1's auc: 0.890233 [300] training's auc: 0.921171 valid_1's auc: 0.894842 [400] training's auc: 0.928109 valid_1's auc: 0.898143 [500] training's auc: 0.933715 valid_1's auc: 0.900248 [600] training's auc: 0.938491 valid_1's auc: 0.901761 [700] training's auc: 0.94278 valid_1's auc: 0.902929 [800] training's auc: 0.946664 valid_1's auc: 0.903839 [900] training's auc: 0.950258 valid_1's auc: 0.904602 [1000] training's auc: 0.9536 valid_1's auc: 0.905219 [1100] training's auc: 0.956829 valid_1's auc: 0.905649 [1200] training's auc: 0.959828 valid_1's auc: 0.906074 [1300] training's auc: 0.962569 valid_1's auc: 0.906437 [1400] training's auc: 0.965005 valid_1's auc: 0.906588 [1500] training's auc: 0.967356 valid_1's auc: 0.906752 [1600] training's auc: 0.969535 valid_1's auc: 0.906936 [1700] training's auc: 0.971541 valid_1's auc: 0.907045 [1800] training's auc: 0.973425 valid_1's auc: 0.90712 [1900] training's auc: 0.975185 valid_1's auc: 0.907303 [2000] training's auc: 0.976793 valid_1's auc: 0.907368 [2100] training's auc: 0.978272 valid_1's auc: 0.907424 [2200] training's auc: 0.979728 valid_1's auc: 0.907493 [2300] training's auc: 0.981056 valid_1's auc: 0.90765 [2400] training's auc: 0.982365 valid_1's auc: 0.907661 Early stopping, best iteration is: [2371] training's auc: 0.982008 valid_1's auc: 0.907688 Partial score of fold 0 is: 0.9076875498471981 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.901375 valid_1's auc: 0.881745 [200] training's auc: 0.912832 valid_1's auc: 0.88787 [300] training's auc: 0.921383 valid_1's auc: 0.892287 [400] training's auc: 0.928448 valid_1's auc: 0.895839 [500] training's auc: 0.934094 valid_1's auc: 0.898101 [600] training's auc: 0.938883 valid_1's auc: 0.899576 [700] training's auc: 0.943077 valid_1's auc: 0.900762 [800] training's auc: 0.946875 valid_1's auc: 0.901635 [900] training's auc: 0.950552 valid_1's auc: 0.902358 [1000] training's auc: 0.953838 valid_1's auc: 0.902926 [1100] training's auc: 0.956973 valid_1's auc: 0.903317 [1200] training's auc: 0.959969 valid_1's auc: 0.903701 [1300] training's auc: 0.962731 valid_1's auc: 0.903856 [1400] training's auc: 0.965369 valid_1's auc: 0.904167 [1500] training's auc: 0.967768 valid_1's auc: 0.904344 [1600] training's auc: 0.969873 valid_1's auc: 0.904428 [1700] training's auc: 0.971883 valid_1's auc: 0.904572 [1800] training's auc: 0.973755 valid_1's auc: 0.904797 [1900] training's auc: 0.975516 valid_1's auc: 0.904936 [2000] training's auc: 0.977164 valid_1's auc: 0.905102 [2100] training's auc: 0.97869 valid_1's auc: 0.905131 Early stopping, best iteration is: [2012] training's auc: 0.977356 valid_1's auc: 0.905141 Partial score of fold 1 is: 0.9051408385938304 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.902038 valid_1's auc: 0.880248 [200] training's auc: 0.913162 valid_1's auc: 0.885394 [300] training's auc: 0.921695 valid_1's auc: 0.890112 [400] training's auc: 0.928666 valid_1's auc: 0.893527 [500] training's auc: 0.934286 valid_1's auc: 0.895723 [600] training's auc: 0.939117 valid_1's auc: 0.897066 [700] training's auc: 0.943321 valid_1's auc: 0.897929 [800] training's auc: 0.94719 valid_1's auc: 0.89869 [900] training's auc: 0.950756 valid_1's auc: 0.899334 [1000] training's auc: 0.954059 valid_1's auc: 0.899639 [1100] training's auc: 0.957321 valid_1's auc: 0.900178 [1200] training's auc: 0.960237 valid_1's auc: 0.900616 [1300] training's auc: 0.962941 valid_1's auc: 0.900887 [1400] training's auc: 0.965365 valid_1's auc: 0.901135 [1500] training's auc: 0.967666 valid_1's auc: 0.901366 [1600] training's auc: 0.969889 valid_1's auc: 0.901528 [1700] training's auc: 0.971895 valid_1's auc: 0.901683 [1800] training's auc: 0.97377 valid_1's auc: 0.901874 [1900] training's auc: 0.975576 valid_1's auc: 0.901867 Early stopping, best iteration is: [1833] training's auc: 0.97439 valid_1's auc: 0.901909 Partial score of fold 2 is: 0.9019085917346749 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.901322 valid_1's auc: 0.88713 [200] training's auc: 0.912892 valid_1's auc: 0.891667 [300] training's auc: 0.921663 valid_1's auc: 0.89551 [400] training's auc: 0.928625 valid_1's auc: 0.898135 [500] training's auc: 0.934207 valid_1's auc: 0.899831 [600] training's auc: 0.938917 valid_1's auc: 0.901065 [700] training's auc: 0.943256 valid_1's auc: 0.901966 [800] training's auc: 0.947072 valid_1's auc: 0.902582 [900] training's auc: 0.950695 valid_1's auc: 0.903042 [1000] training's auc: 0.954213 valid_1's auc: 0.903497 [1100] training's auc: 0.957428 valid_1's auc: 0.903788 [1200] training's auc: 0.960413 valid_1's auc: 0.904064 [1300] training's auc: 0.963176 valid_1's auc: 0.9042 [1400] training's auc: 0.965668 valid_1's auc: 0.904476 [1500] training's auc: 0.967977 valid_1's auc: 0.90459 [1600] training's auc: 0.970152 valid_1's auc: 0.904656 [1700] training's auc: 0.972142 valid_1's auc: 0.904807 [1800] training's auc: 0.973993 valid_1's auc: 0.904885 [1900] training's auc: 0.975774 valid_1's auc: 0.904958 [2000] training's auc: 0.977358 valid_1's auc: 0.904924 Early stopping, best iteration is: [1907] training's auc: 0.975885 valid_1's auc: 0.904976 Partial score of fold 3 is: 0.9049756578498265 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.901845 valid_1's auc: 0.882964 [200] training's auc: 0.912768 valid_1's auc: 0.888405 [300] training's auc: 0.921322 valid_1's auc: 0.893408 [400] training's auc: 0.928296 valid_1's auc: 0.897497 [500] training's auc: 0.933917 valid_1's auc: 0.90005 [600] training's auc: 0.93877 valid_1's auc: 0.901715 [700] training's auc: 0.943146 valid_1's auc: 0.902741 [800] training's auc: 0.947 valid_1's auc: 0.903739 [900] training's auc: 0.950613 valid_1's auc: 0.904419 [1000] training's auc: 0.953997 valid_1's auc: 0.904888 [1100] training's auc: 0.9571 valid_1's auc: 0.905185 [1200] training's auc: 0.960057 valid_1's auc: 0.905574 [1300] training's auc: 0.962835 valid_1's auc: 0.905856 [1400] training's auc: 0.965337 valid_1's auc: 0.906091 [1500] training's auc: 0.96768 valid_1's auc: 0.906274 [1600] training's auc: 0.969904 valid_1's auc: 0.906482 [1700] training's auc: 0.971931 valid_1's auc: 0.906611 [1800] training's auc: 0.973804 valid_1's auc: 0.906896 [1900] training's auc: 0.975505 valid_1's auc: 0.906973 [2000] training's auc: 0.977032 valid_1's auc: 0.90712 [2100] training's auc: 0.978606 valid_1's auc: 0.90722 Early stopping, best iteration is: [2067] training's auc: 0.978098 valid_1's auc: 0.907295 Partial score of fold 4 is: 0.9072954530574024 Our oof AUC score is: 0.9053810190637271 auc: 0.9053810190637271 | 32 | 0.9054 | 0.9233 | 0.7025 | 4.987 | 0.008032 | 16.94 | 1.605 | 1.04 | =============================================================================================================
LGB_BO_v2.max['params']
{'feature_fraction': 0.524207414205945,
'lambda_l1': 4.171808735757517,
'lambda_l2': 4.6435328298317256,
'learning_rate': 0.007897539397989824,
'max_depth': 16.62053004755999,
'scale_pos_weight': 1.2199266532301127,
'subsample_freq': 1.0276518730971627}
if boll_BayesianOptimization: # ACTIVATE it if you want to search/use for better parameter
lgb_model_v2 = Lgb_Model(train,test, features, categoricals=categoricals_features, ps= LGB_BO_v2.max['params'])
else :
lgb_model_v2 = Lgb_Model(train,test, features, categoricals=categoricals_features, ps=params)
Training until validation scores don't improve for 100 rounds [100] training's auc: 0.899358 valid_1's auc: 0.886976 [200] training's auc: 0.907669 valid_1's auc: 0.891791 [300] training's auc: 0.914542 valid_1's auc: 0.895347 [400] training's auc: 0.920361 valid_1's auc: 0.898506 [500] training's auc: 0.92523 valid_1's auc: 0.900896 [600] training's auc: 0.929528 valid_1's auc: 0.902815 [700] training's auc: 0.933281 valid_1's auc: 0.904161 [800] training's auc: 0.936711 valid_1's auc: 0.905167 [900] training's auc: 0.939914 valid_1's auc: 0.905877 [1000] training's auc: 0.942914 valid_1's auc: 0.906541 [1100] training's auc: 0.945717 valid_1's auc: 0.906978 [1200] training's auc: 0.948436 valid_1's auc: 0.907454 [1300] training's auc: 0.950997 valid_1's auc: 0.907894 [1400] training's auc: 0.953357 valid_1's auc: 0.90822 [1500] training's auc: 0.955656 valid_1's auc: 0.90851 [1600] training's auc: 0.957823 valid_1's auc: 0.90876 [1700] training's auc: 0.959913 valid_1's auc: 0.908919 [1800] training's auc: 0.961929 valid_1's auc: 0.9091 [1900] training's auc: 0.963788 valid_1's auc: 0.909298 [2000] training's auc: 0.965544 valid_1's auc: 0.909451 [2100] training's auc: 0.967185 valid_1's auc: 0.909556 [2200] training's auc: 0.968755 valid_1's auc: 0.909665 [2300] training's auc: 0.970308 valid_1's auc: 0.909798 [2400] training's auc: 0.971786 valid_1's auc: 0.909794 [2500] training's auc: 0.973125 valid_1's auc: 0.909857 [2600] training's auc: 0.97446 valid_1's auc: 0.909923 [2700] training's auc: 0.975661 valid_1's auc: 0.909942 Early stopping, best iteration is: [2643] training's auc: 0.974999 valid_1's auc: 0.909959 Partial score of fold 0 is: 0.9099590744138573 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.899598 valid_1's auc: 0.885021 [200] training's auc: 0.907759 valid_1's auc: 0.889878 [300] training's auc: 0.91485 valid_1's auc: 0.893416 [400] training's auc: 0.920711 valid_1's auc: 0.896422 [500] training's auc: 0.925693 valid_1's auc: 0.898995 [600] training's auc: 0.929923 valid_1's auc: 0.900811 [700] training's auc: 0.933601 valid_1's auc: 0.902158 [800] training's auc: 0.936951 valid_1's auc: 0.903225 [900] training's auc: 0.940162 valid_1's auc: 0.904073 [1000] training's auc: 0.943143 valid_1's auc: 0.90483 [1100] training's auc: 0.945996 valid_1's auc: 0.905314 [1200] training's auc: 0.948736 valid_1's auc: 0.905832 [1300] training's auc: 0.951279 valid_1's auc: 0.906225 [1400] training's auc: 0.953673 valid_1's auc: 0.906559 [1500] training's auc: 0.955977 valid_1's auc: 0.906753 [1600] training's auc: 0.958108 valid_1's auc: 0.906796 [1700] training's auc: 0.960178 valid_1's auc: 0.906925 [1800] training's auc: 0.962136 valid_1's auc: 0.907044 [1900] training's auc: 0.964001 valid_1's auc: 0.907168 [2000] training's auc: 0.965698 valid_1's auc: 0.907241 [2100] training's auc: 0.967362 valid_1's auc: 0.907282 Early stopping, best iteration is: [2032] training's auc: 0.966251 valid_1's auc: 0.907315 Partial score of fold 1 is: 0.9073147418047061 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.900782 valid_1's auc: 0.882618 [200] training's auc: 0.908633 valid_1's auc: 0.887012 [300] training's auc: 0.915261 valid_1's auc: 0.890628 [400] training's auc: 0.92104 valid_1's auc: 0.893789 [500] training's auc: 0.925891 valid_1's auc: 0.896184 [600] training's auc: 0.930131 valid_1's auc: 0.897768 [700] training's auc: 0.933908 valid_1's auc: 0.898875 [800] training's auc: 0.937272 valid_1's auc: 0.899895 [900] training's auc: 0.94044 valid_1's auc: 0.900628 [1000] training's auc: 0.943377 valid_1's auc: 0.901196 [1100] training's auc: 0.946279 valid_1's auc: 0.901509 [1200] training's auc: 0.948943 valid_1's auc: 0.901901 [1300] training's auc: 0.951434 valid_1's auc: 0.902266 [1400] training's auc: 0.953821 valid_1's auc: 0.902558 [1500] training's auc: 0.95614 valid_1's auc: 0.902864 [1600] training's auc: 0.958298 valid_1's auc: 0.903075 [1700] training's auc: 0.960317 valid_1's auc: 0.903315 [1800] training's auc: 0.962192 valid_1's auc: 0.903397 [1900] training's auc: 0.964027 valid_1's auc: 0.903598 [2000] training's auc: 0.965784 valid_1's auc: 0.903705 [2100] training's auc: 0.967416 valid_1's auc: 0.903731 [2200] training's auc: 0.969026 valid_1's auc: 0.903885 [2300] training's auc: 0.970572 valid_1's auc: 0.903897 [2400] training's auc: 0.971953 valid_1's auc: 0.904012 [2500] training's auc: 0.973276 valid_1's auc: 0.904062 [2600] training's auc: 0.974591 valid_1's auc: 0.90408 [2700] training's auc: 0.975829 valid_1's auc: 0.904138 [2800] training's auc: 0.977045 valid_1's auc: 0.904177 [2900] training's auc: 0.978182 valid_1's auc: 0.904241 [3000] training's auc: 0.979279 valid_1's auc: 0.904186 Early stopping, best iteration is: [2915] training's auc: 0.978346 valid_1's auc: 0.904257 Partial score of fold 2 is: 0.9042566303369483 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.899869 valid_1's auc: 0.888264 [200] training's auc: 0.907982 valid_1's auc: 0.892879 [300] training's auc: 0.914798 valid_1's auc: 0.896127 [400] training's auc: 0.920801 valid_1's auc: 0.898862 [500] training's auc: 0.925628 valid_1's auc: 0.90076 [600] training's auc: 0.929934 valid_1's auc: 0.902154 [700] training's auc: 0.933667 valid_1's auc: 0.903222 [800] training's auc: 0.937023 valid_1's auc: 0.904127 [900] training's auc: 0.940284 valid_1's auc: 0.904678 [1000] training's auc: 0.943252 valid_1's auc: 0.90518 [1100] training's auc: 0.946159 valid_1's auc: 0.905606 [1200] training's auc: 0.948909 valid_1's auc: 0.905944 [1300] training's auc: 0.951474 valid_1's auc: 0.906274 [1400] training's auc: 0.953954 valid_1's auc: 0.90652 [1500] training's auc: 0.956242 valid_1's auc: 0.906657 [1600] training's auc: 0.958421 valid_1's auc: 0.906822 [1700] training's auc: 0.960432 valid_1's auc: 0.907026 [1800] training's auc: 0.962328 valid_1's auc: 0.907088 [1900] training's auc: 0.964192 valid_1's auc: 0.907162 [2000] training's auc: 0.965914 valid_1's auc: 0.907201 Early stopping, best iteration is: [1967] training's auc: 0.965354 valid_1's auc: 0.907245 Partial score of fold 3 is: 0.907244867854072 Training until validation scores don't improve for 100 rounds [100] training's auc: 0.899802 valid_1's auc: 0.885389 [200] training's auc: 0.907617 valid_1's auc: 0.890191 [300] training's auc: 0.91444 valid_1's auc: 0.893913 [400] training's auc: 0.920195 valid_1's auc: 0.897459 [500] training's auc: 0.925221 valid_1's auc: 0.900295 [600] training's auc: 0.929517 valid_1's auc: 0.902334 [700] training's auc: 0.933258 valid_1's auc: 0.903623 [800] training's auc: 0.936676 valid_1's auc: 0.904537 [900] training's auc: 0.939914 valid_1's auc: 0.905354 [1000] training's auc: 0.942962 valid_1's auc: 0.906036 [1100] training's auc: 0.945841 valid_1's auc: 0.906568 [1200] training's auc: 0.948608 valid_1's auc: 0.907135 [1300] training's auc: 0.951153 valid_1's auc: 0.907547 [1400] training's auc: 0.953636 valid_1's auc: 0.907967 [1500] training's auc: 0.955902 valid_1's auc: 0.908143 [1600] training's auc: 0.958035 valid_1's auc: 0.908402 [1700] training's auc: 0.960098 valid_1's auc: 0.908647 [1800] training's auc: 0.962062 valid_1's auc: 0.908781 [1900] training's auc: 0.963887 valid_1's auc: 0.908894 [2000] training's auc: 0.965633 valid_1's auc: 0.908983 [2100] training's auc: 0.967299 valid_1's auc: 0.90909 [2200] training's auc: 0.968846 valid_1's auc: 0.909291 [2300] training's auc: 0.970359 valid_1's auc: 0.909271 Early stopping, best iteration is: [2237] training's auc: 0.969396 valid_1's auc: 0.909322 Partial score of fold 4 is: 0.9093215751508595 Our oof AUC score is: 0.9075241899054636
# Plot Feat Importance
imp_df_v2 = pd.DataFrame()
imp_df_v2['feature'] = features
imp_df_v2['gain'] = lgb_model_v2.model.feature_importance(importance_type='gain')
imp_df_v2['split'] = lgb_model_v2.model.feature_importance(importance_type='split')
plot_importances(imp_df_v2)
import warnings
warnings.filterwarnings("ignore")
warnings.simplefilter(action='ignore', category=UserWarning)
i=0
for index, row in imp_df_v2.sort_values(by=['gain'],ascending=False).iterrows():
column=row['feature']
if i< 50:
print(column,i,"gain :",row['gain'])
df1 = train.loc[train['hospital_death']==0]
df2 = train.loc[train['hospital_death']==1]
fig = plt.figure(figsize=(20,4))
sns.distplot(df1[column].dropna(), color='red', label='hospital_death 0', kde=True);
sns.distplot(df2[column].dropna(), color='blue', label='hospital_death 1', kde=True);
fig=plt.legend(loc='best')
plt.xlabel(column, fontsize=12);
plt.show()
i=i+1
apache_4a_hospital_death_prob 0 gain : 329680.2181470394
apache_4a_icu_death_prob 1 gain : 156631.5116765499
hospital_id 2 gain : 145410.42276763916
d1_lactate_min 3 gain : 60796.08799648285
d1_spo2_min 4 gain : 32836.89942884445
ventilated_apache 5 gain : 30552.519705295563
age 6 gain : 22173.600246667862
d1_sysbp_min 7 gain : 22129.421936273575
d1_heartrate_min 8 gain : 21593.396679878235
d1_bun_min 9 gain : 17723.877878189087
apache_3j_diagnosis 10 gain : 17544.95256614685
d1_sysbp_noninvasive_min 11 gain : 16455.46886730194
d1_lactate_max 12 gain : 16348.615434885025
d1_temp_max 13 gain : 15951.985478878021
gcs_motor_apache 14 gain : 14751.15071105957
urineoutput_apache 15 gain : 13288.36552143097
d1_bun_max 16 gain : 13152.739966869354
d1_platelets_min 17 gain : 12602.045546770096
gcs_eyes_apache 18 gain : 12534.613032341003
d1_resprate_min 19 gain : 11451.371369123459
d1_arterial_ph_min 20 gain : 11213.823065042496
d1_temp_min 21 gain : 11016.257133483887
bmi 22 gain : 10569.011886119843
d1_resprate_max 23 gain : 10451.489589452744
apache_3j_bodysystem 24 gain : 10363.706548213959
d1_heartrate_max 25 gain : 9830.411767721176
d1_glucose_min 26 gain : 9034.130075931549
wbc_apache 27 gain : 8726.267885684967
d1_wbc_min 28 gain : 8554.629989147186
creatinine_apache 29 gain : 8105.224138975143
h1_resprate_min 30 gain : 7891.371155738831
apache_2_diagnosis 31 gain : 7832.032730102539
d1_sodium_max 32 gain : 7768.3484926223755
d1_pao2fio2ratio_max 33 gain : 7706.5754590034485
pre_icu_los_days 34 gain : 7615.919073820114
d1_arterial_ph_max 35 gain : 7319.393048524857
glucose_apache 36 gain : 7211.341676950455
weight 37 gain : 7177.361359834671
temp_apache 38 gain : 7013.908061504364
d1_platelets_max 39 gain : 6979.343486785889
apache_2_bodysystem 40 gain : 6878.724554777145
d1_arterial_po2_max 41 gain : 6779.606928348541
d1_pao2fio2ratio_min 42 gain : 6671.06258058548
d1_hco3_min 43 gain : 6501.838881731033
d1_sysbp_noninvasive_max 44 gain : 6441.646271944046
d1_mbp_noninvasive_min 45 gain : 6383.477047920227
d1_hco3_max 46 gain : 6359.334758520126
d1_inr_max 47 gain : 5976.186361789703
bun_apache 48 gain : 5789.338826417923
d1_arterial_po2_min 49 gain : 5672.42506146431
test["hospital_death"] = lgb_model_v2.y_pred
test[["encounter_id","hospital_death"]].to_csv("submission4-lgb-v3.csv",index=False)
test[["encounter_id","hospital_death"]].head()
| encounter_id | hospital_death | |
|---|---|---|
| 0 | 2 | 0.01 |
| 1 | 5 | 0.02 |
| 2 | 7 | 0.01 |
| 3 | 8 | 0.12 |
| 4 | 10 | 0.66 |